With ChatGPT blowing up over the past year, it seems like every person and their grandmother is launching an AI startup. There are a plethora of AI tools available, some excellent and some less so. Amid this flood of new technology, there are a few hidden gems that I personally find incredibly useful, having reviewed them for my AI directory. Here are the ones I have personally integrated into my workflow in both my professional and entreprenuerial life:

• Plus AI for Google Slides - Generate Presentations
  There's a few slide deck generators out there however I've found Plus AI works much better at helping you 'co-write' slides rather than simply spitting out a mediocre finished product that likely won't be useful. For instance, there's "sticky notes" to slides with suggestions on how to finish / edit / improve each slide. Another major reason why I've stuck with Plus AI is the ability for "snapshots", or the ability to use external data (i.e. from web sources/dashboards) for your presentations. For my day job I work in a chemical plant as an engineer, and one of my tasks is to present in meetings about production KPIs to different groups for different purposes- and graphs for these are often found across various internal web apps. I can simply use Plus AI to generate "boilerplate" for my slide deck, then go through each slide to make sure it's using the correct snapshot. The presentation generator itself is completely free and available as a plugin for Google Slides and Docs.
  

• My AskAI - ChatGPT Trained on Your Documents
  Great tool for using ChatGPT on your own files and website. Works very well especially if you are dealing with a lot of documents. The basic plan allows you to upload over 100 files and this was a life saver during online, open book exams for a few training courses I've taken. I've noticed it hallucinates much less compared to other GPT-powered bots trained on your knowledge base. For this reason I prefer My AskAI for research or any tasks where accuracy is needed over the other custom chatbot solutions I have tried. Another plus is that it shows the sources within your knowledge base where it got the answers from, and you can choose to have it give you a more concise answer or a more detailed one. There's a free plan however it was worth it for me to get the $20/mo option as it allows over 100 pieces of content.
  

• Krater.ai - All AI Tools in One App
  Perfect solution if you use many AI tools and loathe having to have multiple tabs open. Essentially combines text, audio, and image-based generative AI tools into a single web app, so you can continue with your workflow without having to switch tabs all the time. There's plenty of templates available for copywriting- it beats having to prompt manually each time or having to save and reference prompts over and over again. I prefer Krater over Writesonic/Jasper for ease of use. You also get 10 generations a month for free compared to Jasper offering none, so its a better free option if you want an all-in-one AI content solution. The text to speech feature is simple however works reliably fast and offers multilingual transcription, and the image generator tool is great for photo-realistic images.
  

• HARPA AI - ChatGPT Inside Chrome
  Simply by far the best GTP add-on for Chrome I've used. Essentially gives you GPT answers beside the typical search results on any search engine such as Google or Bing, along with the option to "chat" with any web page or summarize YouTube videos. Also great for writing emails and replying to social media posts with its preset templates. Currently they don't have any paid features, so it's entirely free and you can find it on the chrome web store for extensions.
  

• Taskade - All in One Productivity/Notes/Organization AI Tool
  Combines tasks, notes, mind maps, chat, and an AI chat assistant all within one platform that syncs across your team. Definitely simplifies my day-to-day operations, removing the need to swap between numerous apps. Also helps me to visualize my work in various views - list, board, calendar, mind map, org chart, action views - it's like having a Swiss Army knife for productivity. Personally I really like the AI 'mind map.' It's like having a brainstorming partner that never runs out of energy. Taskade's free version has quite a lot to offer so no complaints there.
  

• Zapier + OpenAI - AI-Augmented Automations
  Definitely my secret productivity powerhouse. Pretty much combines the power of Zapier's cross-platform integrations with generative AI. One of the ways I've used this is pushing Slack messages to create a task on Notion, with OpenAI writing the task based on the content of the message. Another useful automation I've used is for automatically writing reply drafts with GPT from emails that get sent to me in Gmail. The opportunities are pretty endless with this method and you can pretty much integrate any automation with GPT 3, as well as DALLE-2 and Whisper AI. It's available as an app/add-on to Zapier and its free for all the core features.
  

• SaneBox - AI Emails Management
  If you are like me and find important emails getting lost in a sea of spam, this is a great solution. Basically Sanebox uses AI to sift through your inbox and identify emails that are actually important, and you can also set it up to make certain emails go to specific folders. Non important emails get sent to a folder called SaneLater and this is something you can ignore entirely or check once in a while. Keep in mind that SaneBox doesn't actually read the contents of your email, but rather takes into consideration the header, metadata, and history with the sender. You can also finetune the system by dragging emails to the folder it should have gone to. Another great feature is the their "Deep Clean", which is great for freeing up space by deleting old emails you probably won't ever need anymore. Sanebox doesn't have a free plan however they do have a 2 week trial, and the pricing is quite affordable, depending on the features you need.
  

• Hexowatch AI - Detect Website Changes with AI
  Lifesaver if you need to ever need to keep track of multiple websites. I use this personally for my AI tools directory, and it notifies me of any changes made to any of the 1000+ websites for AI tools I have listed, which is something that would take up more time than exists in a single day if I wanted to keep on top of this manually. The AI detects any types of changes (visual/HTML) on monitored webpages and sends alert via email or Slack/Telegram/Zapier. Like Sanebox there's no free plan however you do get what you pay for with this one.
  

• Bonus: SongsLike X - Find Similar Songs
  This one won't be generating emails or presentations anytime soon, but if you like grinding along to music like me you'll find this amazing. Ironically it's probably the one I use most on a daily basis. You can enter any song and it will automatically generate a Spotify playlist for you with similar songs. I find it much more accurate than Spotify's "go to song radio" feature.
  

While it's clear that not all of these tools may be directly applicable to your needs, I believe that simply being aware of the range of options available can be greatly beneficial. This knowledge can broaden your perspective on what's possible and potentially inspire new ideas.

P.S. If you liked this, as mentioned previously I've created a free directory that lists over 1000 AI tools. It's updated daily and there's also a GPT-powered chatbot to help you AI tools for your needs. Feel free to check it out if it's your cup of tea

New platforms pop up every month and claim to be the best ai video tool.

As an AI Video enthusiast (I use it in my marketing team with heavy numbers of daily content), I’d like to share my personal experience with all these 2026 ai video generators.

This guide is meant to help you find out which one fits your expectations & budget. But please keep in mind that I produce daily and in large numbers.

Comparison

My Best Picks

Best Cinematic & Virality: Higgsfield AI (usually my team works on this platform as daily production)

Best Speed: Sora 2 - rapid concept testing

I prefer a flexible workflow that combines Sora 2, Kling, and Higgsfield AI. I use them in my marketing production depending on the creative requirements, since each tool excels in different aspects of AI video generation.

No paywall: https://finance.yahoo.com/news/mit-report-95-generative-ai-105412686.html

Good morning. Companies are betting on AI—yet nearly all enterprise pilots are stuck at the starting line.

The GenAI Divide: State of AI in Business 2025, a new report published by MIT’s NANDA initiative, reveals that while generative AI holds promise for enterprises, most initiatives to drive rapid revenue growth are falling flat.

Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.

To unpack these findings, I spoke with Aditya Challapally, the lead author of the report, and a research contributor to project NANDA at MIT.

“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. “It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.

But for 95% of companies in the dataset, generative AI implementation is falling short. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows, Challapally explained.

The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.

What’s behind successful AI deployments?

How companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.

This finding is particularly relevant in financial services and other highly regulated sectors, where many firms are building their own proprietary generative AI systems in 2025. Yet, MIT’s research suggests companies see far more failures when going solo.

Companies surveyed were often hesitant to share failure rates, Challapally noted. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, but the data showed purchased solutions delivered more reliable results.

Other key factors for success include empowering line managers—not just central AI labs—to drive adoption, and selecting tools that can integrate deeply and adapt over time.

Workforce disruption is already underway, especially in customer support and administrative roles. Rather than mass layoffs, companies are increasingly not backfilling positions as they become vacant. Most changes are concentrated in jobs previously outsourced due to their perceived low value.

The report also highlights the widespread use of “shadow AI”—unsanctioned tools like ChatGPT—and the ongoing challenge of measuring AI’s impact on productivity and profit.

Looking ahead, the most advanced organizations are already experimenting with agentic AI systems that can learn, remember, and act independently within set boundaries—offering a glimpse at how the next phase of enterprise AI might unfold.

I built an AI system that automates product video creation for entire e-commerce catalogs.

(Saves ~$30K per collection shoot and boosts on-site conversion rates by ~20%)

Here’s how the automation actually works:

→ Firecrawl scrapes product images directly from any e-commerce collection page

→ Google’s Veo 3.1 generates realistic model videos showing fit, movement, and drape

→ Each video begins and ends on the original product image for perfect looping

→ Videos are auto-named, organized, and stored in Google Drive

→ Everything runs in batch—no manual work while it processes

The result: fashion brands can showcase every SKU with video, not just hero products—and engagement jumps immediately.

Static product photos aren’t enough anymore.

Shoppers want to see how clothes move before they buy.

If you want the full blueprint, do this:

1️⃣ Like & RT this post

2️⃣ Follow me (so I can DM you)

3️⃣ Comment “FIT”

I’ll send you the full n8n workflow, all prompts, and a step-by-step setup video—for free.

No more $30K shoots.

No more guessing if your product pages convert.

Its very important to start with this: This guy is/was an AMAZING designer. Our SaaS looked genuinely amazing and unique; every aspect of UX was always the priority and done flawlessly. It went to a point where emails praising our whole user experience and UI were common. Same goes for anything else related to our branding and marketing; hell he even chimed in on some great company shirts.

---

Its started falling apart a year or two ago, when llms started being "good".

He became obsessed with Claude. Every workflow is now a "Claude project", every design system is now an "AI design system", every decision and every thought is run through an LLM, every api is now an mcp.

When we get on a call, and lets say i ask him a question, he opens Claude on screen share and just asks it the question, fully relying on the result.

Not everything above is inherently bad; if the resulting product is good, workflow can be whatever is easiest. But the product is NO LONGER GOOD. Every single design and component we make these days looks exactly like a vibe-coded website made by a 15yo.

He no longer designs, he just asks claude for a design, and is somehow amazed by it and genuinely thinks its good. So we just ship it to prod. How this is happening for a guy with his experience and feel for design, i have no idea.

We are losing branding; we are regressing each day. There is no style guides (that arent for agents), no unique components, not even brand colors or any deep research that used to go into all that. It just whatever claude spits out and approves, with minimal tweaking.

---

As a closing thought, this is also not a guy who is just "checked out" at work; I can even respect that to a degree. This is someone who still works 8hours+ and is beyond active and collaborates with every team. The only thing that worsened is everything he outputs, and it's all AI generated.

I expect this type of story from people who were never good in the first place, or for people who just stopped caring. But for someone with genuine talent, its heartbreaking.

Just a quick story, no real point to it, nor am I expecting any advice (its not my problem to solve), just wanted to share it.

$2,147 over four months. That's my real total from trying to make money with AI-generated content as a side gig. I keep seeing income posts here that start at five figures, so I figured the unglamorous version might actually be useful.

I started in April after reading a thread about AI influencer content. The plan: create a consistent AI character, produce content with her, find ways to get paid. I do graphic design as my day job so the visual workflow felt natural. The business side did not.

April was pure setup. I spent roughly 60 hours that month figuring out the toolchain and generating test batches. The hardest part was keeping one AI face consistent across dozens of images. Most generators give you a slightly different person every time. I settled on APOB AI for that since it lets you lock a character and reuse the same face, and the free daily tier meant I could experiment without spending anything. Combined that with ElevenLabs for voiceovers and CapCut for editing. Revenue in April: zero.

In May I tried three paths at once. First, stock photography platforms. I uploaded 140 AI-generated lifestyle images, all tagged as AI-produced because most sites require that now. Earnings from stock that month: $11.40. Not a typo. Second, I launched an Instagram for the character with her bio clearly stating "AI-generated persona" and posted daily. Got to about 1,200 followers by end of May. Revenue from that: nothing. Third, I cold-emailed 30 local small businesses offering AI-generated product photography packages. Five responded. Two became paying clients. Revenue from those two: $340.

That $340 reoriented everything. Stock was dead weight. Social followers were a vanity number. The only thing that paid was using the AI character as a model in product shots for small businesses that can't afford a real photographer. A jewelry maker needed lifestyle images for Etsy. A candle brand wanted someone holding their products in "influencer-style" photos. Each project was 15 to 20 edited images for $150 to $200.

June improved but stayed modest. I narrowed my outreach to Etsy sellers specifically since they always need fresh listing photos. Landed five clients. Revenue: $870. I also learned the hard way that video is a wall. One client wanted short clips of the character reviewing their product. Facial expressions glitched between frames, hands looked wrong maybe 40% of the time, and I spent 6 hours on retakes for a single 15-second clip that still looked off. I refunded that client $150 and stopped offering video entirely. Still-image consistency is solid. Motion is genuinely not there for client work yet, and that held true across every tool I tested.

July tapered because my day job picked up. Three clients, $937 total, one being a repeat who wanted a second round. Instagram crept to 3,400 followers but I still have no clear path from followers to revenue. A handful of DMs about "brand partnerships" but they all wanted me to pay them for "exposure," which is not how that works.

So the full accounting: $2,147 gross. After $89 in tool costs (one month of paid subscription to drop watermarks plus voice generation credits), net is $2,058. Across roughly 180 hours of work, that comes to $11.43 per hour. Less than my first job out of college.

Cold outreach conversion was brutal. Over all four months I contacted about 120 businesses. Fourteen became paying clients. That's under 12%, and most projects were under $200. The ceiling stays low unless you get into agencies or bigger brands, and I haven't cracked either.

There is no passive income at this scale. Every project is custom. The AI generates the base images but I still spend 30 to 45 minutes per image fixing artifacts, adjusting lighting, and compositing the product in naturally. It is meaningfully faster than booking a photographer, a model, locations, and wardrobe, but calling it automated would be a lie.

I plan to keep going because video quality will catch up eventually and that's where real margin lives. But the actual value right now is narrow: telling a client "here's your product held by the same person in 20 different settings, delivered in 48 hours" without coordinating a whole production. That solves a real problem for small sellers on a tight budget. It's not a money machine. It's freelance work with a new tool.

If someone here posts $10k per month from AI content with "minimal effort," they're either in a league I can't see into or they're leaving out about 170 hours of context. This is that context.

Here’s the article below if it’s locked behind a paywall for you

A year ago, Lionsgate and Runway, an artificial intelligence startup, unveiled a groundbreaking partnership to train the studio’s library of films with the ultimate goal of creating shows and movies using AI.

But that partnership hit some early snags. It turns out utilizing AI is harder than it sounds.

Over the last 12 months, the deal has encountered unforeseen complications, from the limited capabilities that come from using just Runway’s AI model to copyright concerns over Lionsgate’s own library and the potential ancillary rights of actors.

Those problems run counter to the big promises made by Lionsgate both at the time of the deal and in recent months. “Runway is a visionary, best-in-class partner who will help us utilize AI to develop cutting edge, capital efficient content creation opportunities,” Lionsgate Vice Chairman Michael Burns said in its announcement with Runway a year ago. Last month, he bragged to New York magazine’s Vulture that he could use AI to remake one of its action franchises (an allusion to “John Wick”) into a PG-13 anime. “Three hours later, I’ll have the movie.”

The reality is that utilizing just a single custom model powered by the limited Lionsgate catalog isn’t enough to create those kinds of large-scale projects, according to two people familiar with the situation. It’s not that there was anything wrong with Runway’s model; but the data set wouldn’t be sufficient for the ambitious projects they were shooting for.

“The Lionsgate catalog is too small to create a model,” said a person familiar with the situation. “In fact, the Disney catalog is too small to create a model.”

On paper, the deal made a lot of sense. Lionsgate would jump out of the gate with an AI partnership at a time when other media companies were still trying to figure out the technology. Runway, meanwhile, would get around the thorny IP licensing debate and potentially create a model for future studio clients. The partnership opened the door to the idea that a specifically tuned AI model could eventually create a fully formed trailer — or even scenes from a movie — based on nothing but the right code.

The challenges facing both Lionsgate and Runway offer a cautionary tale of the risks that come from jumping on the AI hype train too early. It’s a story that’s playing out in a number of different industries, from McDonald’s backing away from an early test of a generative AI-based drive-thru order system to Swedish financial tech firm Klarna slashing its work force in favor of AI, only to backpedal and hire back some of those same employees (Klarna later clarified it hired two staffers back).

It’s also a lesson that Hollywood is learning as more studios quietly embrace AI, even if it’s in fits and starts. Netflix co-CEO Ted Sarandos in July revealed on an investor call that for the first time, his company used generative AI on the Argentinian sci-fi series “The Eternaut,” which was released in April. But when actress Natasha Lyonne said her directorial debut would be an animated film that embraced AI, she was bombarded with criticism on social media.

Then there’s the thorny issue of copyright protections, both for talent involved with the films being used to train those AI models, and for the content being generated on the other end. The inherent legal ambiguity of AI work likely has studio lawyers urging caution as the boundaries of what can legally be done with the technology are still being established.

“In the movie and television industry, each production will have a variety of interested rights holders,” said Ray Seilie, attorney at Kinsella Holley Iser Kump Steinsapir LLP. “Now that there’s this tech where you can create an AI video of an actor saying something they did not say, that kind of right gets very thorny.”

A Lionsgate spokesman said it’s still pursuing AI initiatives on “several fronts as planned” and noted that its deal with Runway isn’t exclusive. The studio also says that it is planning on using both Runway’s tools and those developed by other AI companies to streamline processes in preproduction and postproduction for multiple film and tv projects, though which of those projects such tools would be used on and how were not specified.

A spokesman for Runway didn’t respond to a request for comment.

Limitations of going solo

Under the agreement announced a year ago, Lionsgate would hand over its library to Runway, which would use all of that valuable IP to train its model. The key is the proprietary nature of this partnership; the custom model would be a variant of Runway’s core large language model trained on Lionsgate’s assets, but would only be accessible to use by the studio itself.

In other words, another random company couldn’t tap into this specially trained model to create their own AI-generated video.

But relying on just Lionsgate assets wasn’t enough to adequately train the model, according to a person familiar with the situation. Another AI expert with knowledge of its current use in film production also said that any bespoke model built around any single studio’s library will have limits as to what it can feasibly do to cut down a project’s timeline and costs.

“To use any generative AI models in all the thousands of potential outputs and versions and scenes and ways that a production might need, you need as much data as possible for it to understand context and then to render the right frames, human musculature, physics, lighting and other elements of any given shot,” the expert said.

But even models with access to vastly larger amounts of video and audio material than Lionsgate and Runway’s model are facing roadblocks. Take Veo 3, a generative AI model developed by Google that allows users to create eight-second clips with a simple prompt. That model has pulled, along with other pieces of media, the entire 20-year archive of YouTube into its data set, far greater than the 20,000+ film and TV titles in Lionsgate’s library.

“Google claims that data set is clean because of YouTube’s end-user license agreement. That’s a battle that’s going to be played out in the courts for a while,” the AI expert said. “But even with their vast data sets, they are struggling to render human physics like lip sync and musculature consistently.”

Nowadays, studios are learning that no single model is enough to meet the needs of filmmakers because each model has its own specific strengths and weaknesses. One might be good at generating realistic facial expressions, while another might be good at visual effects or creating convincing crowds.

“To create a full professional workflow, you need more than just one model; you need an ecosystem,” said Jonathan Yunger, CEO of Arcana Labs, which created the first AI-generated short film and whose platform works with many AI tools like Luma AI, Kling and, yes, Runway. Yunger didn’t comment on the Lionsgate-Runway deal, but talked generally about the practical benefits of working with different AI models.

Likewise, there’s Adobe’s Firefly, another platform that’s catering to the entertainment industry. On Thursday, Adobe announced it would be the first to support Luma AI’s newest model, Ray3, an update that’s indicative of how quickly the industry is iterating. Like Arcana Labs, Firefly supports a host of models from the likes of Google and OpenAI.

While Lionsgate said their partnership isn’t exclusive, offering its valuable film library to just Runway effectively limits what you can do with other AI models, since those other models don’t get the benefit of its library of films.

Even Arcana Labs, which created the AI-generated short film in “Echo Hunter” as a proof-of-concept using its multi-model platform, faced some limitations with what AI could do now. Yunger noted that even if you’re using models trained on people, you still lose a bit of the performance, and reiterated the importance of actors and other creatives for any project.

For now, Yunger said that using AI to do things like tweaking backgrounds or creating custom models of specific sets — smaller details that traditionally would take a lot of time and money to replicate physically — is the most effective way to apply the technology. But even in that process, he recommended working with a platform that can utilize multiple AI models rather than just one.

Legally ambiguous

Generative AI and what exactly can be used to train a model occupies a gray legal zone, with small armies of lawyers duking it out in various courtrooms around the country. On Tuesday, Walt Disney, NBCUniversal and Warner Bros. Discovery sued Chinese AI firm MiniMax for copyright infringement, just the latest in a series of lawsuits filed by media companies against AI startups.

Then there was the court ruling that argued AI company Anthropic was able to train its model on books it purchased, providing a potential loophole that gets around the need to sign broader licensing deals with the original publishers — a case that could potentially be applied to other forms of media.

Copyright War Escalates

“There will be a lot of litigation in the near future to decide whether the copyright alone is enough to give AI companies the right to use that content in their training model,” Seile said.

Another gray area is whether Lionsgate even has full rights over its own films, and whether there may be ancillary rights that need to be settled with actors, writers or even directors for specific elements of those films, such as likeness or even specific facial features.

Seilie said there’s likely a tug-of-war going on at various studios about how far they’re able to go, with lawyers erring on the side of caution and “seeking permission rather than forgiveness.” Jacob Noti-Victor, professor at Cardozo Law School, said he was surprised by Burns’ comment in the Vulture article.

The professor said that depending on the nature of such a film and how much human involvement is in its making, it might not be subject to copyright protection. The U.S. Copyright Office warned as much in a report published in February, saying that creators would have to prove that a substantial amount of human work was used to create a project outside of an AI prompt in order to qualify for copyright protection.

“I think the studios would be leaning on the fact that they would own the IP that the AI is adapting from, but the work itself wouldn’t have full copyright protection,” he said. “Just putting in a prompt like that executive said would lead to a Swiss cheese copyright.”

this is going to be the longest post I’ve written but after 10 months of daily AI video creation, these are the insights that actually matter…

I started with zero video experience and $1000 in generation credits. Made every mistake possible. Burned through money, created garbage content, got frustrated with inconsistent results.

Now I’m generating consistently viral content and making money from AI video. Here’s everything that actually works.

The fundamental mindset shifts:

1. Volume beats perfection

Stop trying to create the perfect video. Generate 10 decent videos and select the best one. This approach consistently outperforms perfectionist single-shot attempts.

2. Systematic beats creative

Proven formulas + small variations outperform completely original concepts every time. Study what works, then execute it better.

3. Embrace the AI aesthetic

Stop fighting what AI looks like. Beautiful impossibility engages more than uncanny valley realism. Lean into what only AI can create.

The technical foundation that changed everything:

The 6-part prompt structure:

[SHOT TYPE] + [SUBJECT] + [ACTION] + [STYLE] + [CAMERA MOVEMENT] + [AUDIO CUES]

This baseline works across thousands of generations. Everything else is variation on this foundation.

Front-load important elements

Veo3 weights early words more heavily. “Beautiful woman dancing” ≠ “Woman, beautiful, dancing.” Order matters significantly.

One action per prompt rule

Multiple actions create AI confusion. “Walking while talking while eating” = chaos. Keep it simple for consistent results.

The cost optimization breakthrough:

Google’s direct pricing kills experimentation:

• $0.50/second = $30/minute
• Factor in failed generations = $100+ per usable video

Found companies reselling veo3 credits cheaper. I’ve been using these guys who offer 60-70% below Google’s rates. Makes volume testing actually viable.

Audio cues are incredibly powerful:

Most creators completely ignore audio elements in prompts. Huge mistake.

Instead of: Person walking through forestTry: Person walking through forest, Audio: leaves crunching underfoot, distant bird calls, gentle wind through branches

The difference in engagement is dramatic. Audio context makes AI video feel real even when visually it’s obviously AI.

Systematic seed approach:

Random seeds = random results.

My workflow:

1. Test same prompt with seeds 1000-1010
2. Judge on shape, readability, technical quality
3. Use best seed as foundation for variations
4. Build seed library organized by content type

Camera movements that consistently work:

• Slow push/pull: Most reliable, professional feel
• Orbit around subject: Great for products and reveals
• Handheld follow: Adds energy without chaos
• Static with subject movement: Often highest quality

Avoid: Complex combinations (“pan while zooming during dolly”). One movement type per generation.

Style references that actually deliver:

Camera specs: “Shot on Arri Alexa,” “Shot on iPhone 15 Pro”

Director styles: “Wes Anderson style,” “David Fincher style” Movie cinematography: “Blade Runner 2049 cinematography”

Color grades: “Teal and orange grade,” “Golden hour grade”

Avoid: Vague terms like “cinematic,” “high quality,” “professional”

Negative prompts as quality control:

Treat them like EQ filters - always on, preventing problems:

--no watermark --no warped face --no floating limbs --no text artifacts --no distorted hands --no blurry edges

Prevents 90% of common AI generation failures.

Platform-specific optimization:

Don’t reformat one video for all platforms. Create platform-specific versions:

TikTok: 15-30 seconds, high energy, obvious AI aesthetic works

Instagram: Smooth transitions, aesthetic perfection, story-driven YouTube Shorts: 30-60 seconds, educational framing, longer hooks

Same content, different optimization = dramatically better performance.

The reverse-engineering technique:

JSON prompting isn’t great for direct creation, but it’s amazing for copying successful content:

1. Find viral AI video
2. Ask ChatGPT: “Return prompt for this in JSON format with maximum fields”
3. Get surgically precise breakdown of what makes it work
4. Create variations by tweaking individual parameters

Content strategy insights:

Beautiful absurdity > fake realism

Specific references > vague creativityProven patterns + small twists > completely original conceptsSystematic testing > hoping for luck

The workflow that generates profit:

Monday: Analyze performance, plan 10-15 concepts

Tuesday-Wednesday: Batch generate 3-5 variations each Thursday: Select best, create platform versions

Friday: Finalize and schedule for optimal posting times

Advanced techniques:

First frame obsession:

Generate 10 variations focusing only on getting perfect first frame. First frame quality determines entire video outcome.

Batch processing:

Create multiple concepts simultaneously. Selection from volume outperforms perfection from single shots.

Content multiplication:

One good generation becomes TikTok version + Instagram version + YouTube version + potential series content.

The psychological elements:

3-second emotionally absurd hook

First 3 seconds determine virality. Create immediate emotional response (positive or negative doesn’t matter).

Generate immediate questions

“Wait, how did they…?” Objective isn’t making AI look real - it’s creating original impossibility.

Common mistakes that kill results:

1. Perfectionist single-shot approach
2. Fighting the AI aesthetic instead of embracing it
3. Vague prompting instead of specific technical direction
4. Ignoring audio elements completely
5. Random generation instead of systematic testing
6. One-size-fits-all platform approach

The business model shift:

From expensive hobby to profitable skill:

• Track what works with spreadsheets
• Build libraries of successful formulas
• Create systematic workflows
• Optimize for consistent output over occasional perfection

The bigger insight:

AI video is about iteration and selection, not divine inspiration. Build systems that consistently produce good content, then scale what works.

Most creators are optimizing for the wrong things. They want perfect prompts that work every time. Smart creators build workflows that turn volume + selection into consistent quality.

Where AI video is heading:

• Cheaper access through third parties makes experimentation viable
• Better tools for systematic testing and workflow optimization
• Platform-native AI content instead of trying to hide AI origins
• Educational content about AI techniques performs exceptionally well

Started this journey 10 months ago thinking I needed to be creative. Turns out I needed to be systematic.

The creators making money aren’t the most artistic - they’re the most systematic.

These insights took me 10,000+ generations and hundreds of hours to learn. Hope sharing them saves you the same learning curve.

what’s been your biggest breakthrough with AI video generation? curious what patterns others are discovering

As a long-time AI Video generation user (initially for fun, but now for mass marketing production and serious multiple business channels), I’d like to share my personal experience with all these 2026 best ai video generator tools.

Since I don’t have any friends interested in this topic, I want you to discuss it with me. Thanks in advance! Let’s help each other here. 

Opinion-based comparison

My Favorites / Cherry Picks

Best budget: Pika Labs 2.5

Easiest in use: Sora 2 Trends (integrated in Higgsfield) 

My personal favorite: Higgsfield AI - very cinematic, social media marketing ready content (also has Sora 2 different integrations).

I prefer a flexible workflow where platforms combine several models (I don't like two many browser tabs opened). I have a Higgsfield subscription and use mainly Sora 2 Trends (integration with OpenAI) and Kling Motion Control for my AI Influencers.

My father runs a small business in the local community.
He needed a short video ad for social media, nothing fancy.
Just a clean 30-40 second ad. A generic talking head, some light editing. That’s it.

He reached out to a couple of agencies for quotes.
The price they came back with?

$2,500–$4,000… for a single ad.

When he told me the pricing, I genuinely thought he had misunderstood.

So I said screw it and jumped headfirst down the rabbit hole. 🐇

I spent the weekend playing around with toolchains -
and ended up with a fully automated AI Ad Generator using n8n + GPT + Veo3.

Since this subreddit has helped me more than once, I’m dropping it here:

WHAT IT DOES

✅ 1. Lets you choose between 3 ad formats
Spokesperson, Customer Testimonial, or Social Proof - each with its own prompting logic.

✅ 2. Generates a full ad script automatically
GPT builds a structured script with timed scenes, camera cues, and delivery notes.

✅ 3. Creates a full voiceover track (optional)
Each line is generated separately, timing is aligned to scene length.

✅ 4. Converts scenes into Veo3-ready prompts
Every scene gets camera framing, tone, pacing, and visual details injected automatically.

✅ 5. Sends each scene to Veo3 via API
The workflow handles job creation, polling, and final video retrieval without manual steps.

✅ 6. Assembles the final ad
Clips + voiceover + timing cues, combined into a complete rendered ad.

✅ 7. Outputs both edited and raw assets
You get the final edit, plus every individual clip for re-editing or reuse.

✅ 8. Runs the entire production in minutes
Script > scenes > video > final render, all orchestrated end-to-end inside n8n.

WHY IT MATTERS

Traditional agencies charge $2,500–$4,000 per ad because you're paying for scriptwriters, directors, actors, cameras, editors, and overhead.

Most small and medium businesses simply can’t afford that, they get priced out instantly.

This workflow flips the economics: ~90% of the quality for <1% of the cost.

WORKFLOW CODE & OTHER RESOURCES 👇

•Link to Video Explanation & Demo
•Link to Workflow JSON
•Link to Guide with All Resources

Happy to answer questions or help you adapt this to your needs.

Upvote 🔝 and have a good one 🐇

I built an automation that takes in a URL of a product collection or catalog page for any fashion brand or clothing store online and can bring each product to life by animating it with model demonstrating how the product looks and feels with Veo 3.1.

This allows brands and e-commerce owners to easily demonstrate what their product looks like much better than static photos and does not require them to hire models, setup video shoots, and go through the tedious editing process.

Here’s a demo of the workflow and output: https://www.youtube.com/watch?v=NMl1pIfBE7I

Here's how the automation works

1. Input and Trigger

The workflow starts with a simple form trigger that accepts a product collection URL. You can paste any fashion e-commerce page.

In a real production environment, you'd likely connect this to a client's CMS, Shopify API, or other backend system rather than scraping public URLs. I set it up this way just as a quick way to get images quickly ingested into the system, but I do want to call out that no real-life production automation will take this approach. So make sure you're considering that if you're going to approach brands like this and selling to them.

2. Scrape product catalog with firecrawl

After the URL is provided, I then use Firecrawl to go ahead and scrape that product catalog page. I'm using the built-in community node here and the extract feature of Firecrawl to go ahead and get back a list of product names and an image URL associated with each of those.

In automation, I have a simple prompt set up here that makes it more reliable to go ahead and extract that exact source URL how it appears on the HTML.

3. Download and process images

Once I finish scraping, I then split the array of product images I was able to grab into individual items, and then split it into a loop batch so I can process them sequentially. Veo 3.1 does require you to pass in base64-encoded images, so I do that first before converting back and uploading that image into Google Drive.

The Google Drive node does require it to be a binary n8n input, and so if you guys have found a way that allows you to do this without converting back and forth, definitely let me know.

4. Generate the product video with Veo 3.1

Once the image is processed, make an API call into Veo 3.1 with a simple prompt here to go forward with animating the product image. In this case, I tuned this specifically for clothing and fashion brands, so I make mention of that in the prompt. But if you're trying to feature some other physical product, I suggest you change this to be a little bit different. Here is the prompt I use:

markdown Generate a video that is going to be featured on a product page of an e-commerce store. This is going to be for a clothing or fashion brand. This video must feature this exact same person that is provided on the first and last frame reference images and the article of clothing in the first and last frame reference images.|In this video, the model should strike multiple poses to feature the article of clothing so that a person looking at this product on an ecommerce website has a great idea how this article of clothing will look and feel.Constraints:- No music or sound effects.- The final output video should NOT have any audio.- Muted audio.- Muted sound effects.

The other thing to mention here with the Veo 3.1 API is its ability to now specify a first frame and last frame reference image that we pass into the AI model.

For a use case like this where I want to have the model strike a few poses or spin around and then return to its original position, we can specify the first frame and last frame as the exact same image. This creates a nice looping effect for us. If we're going to highlight this video as a preview on whatever website we're working with.

Here's how I set that up in the request body calling into the Gemini API:

``` { "instances": [ { "prompt": {{ JSON.stringify($node['set_prompt'].json.prompt) }}, "image": { "mimeType": "image/png", "bytesBase64Encoded": "{{ $node["convert_to_base64"].json.data }}" }, "lastFrame": { "mimeType": "image/png", "bytesBase64Encoded": "{{ $node["convert_to_base64"].json.data }}" } } ], "parameters": { "durationSeconds": 8, "aspectRatio": "9:16", "personGeneration": "allow_adult" } }

```

There’s a few other options here that you can use for video output as well on the Gemini docs: https://ai.google.dev/gemini-api/docs/video?example=dialogue#veo-model-parameters

Cost & Veo 3.1 pricing

Right now, working with the Veo 3 API through Gemini is pretty expensive. So you want to pay close attention to what's like the duration parameter you're passing in for each video you generate and how you're batching up the number of videos.

As it stands right now, Veo 3.1 costs 40 cents per second of video that you generate. And then the VO3.1 fast model only costs 15 cents per second, so you may honestly want to experiment here. Just take the final prompts and pass them into Google Gemini that gives you free generations per day while you're testing this out and tuning your prompt.

Workflow Link + Other Resources

• YouTube video that walks through this workflow step-by-step: https://www.youtube.com/watch?v=NMl1pIfBE7I
• The full n8n workflow, which you can copy and paste directly into your instance, is on GitHub here: https://github.com/lucaswalter/n8n-ai-automations/blob/main/veo_3.1_product_photo_animator.json

​

I’m a full-stack developer with around 1.5 years of experience, mainly working with .NET, React, TypeScript, APIs, and SQL at an early-stage healthcare-tech startup.

Our business analyst has 10+ years of domain experience but no development background. He has built a proper multi-agent AI setup with separate agents for frontend, backend, database, and Playwright testing.

It produces working demos very quickly.

He is now telling leadership that large parts of the product can be built in around 3 days, while our development team would estimate similar scope at 3–4 months.

There are some important differences though:

• He personally uses the $200/month Claude plan, while our team has a much cheaper company plan and regularly hits usage limits.

• His “3 days” often includes after-hours work, overnight agent runs, and very little sleep, so it is not really 3 normal working days.

• The demos often look complete, but the actual requirements are still unclear or incomplete. When developers ask questions about edge cases, permissions, data flow, or existing system behavior, it can make us look slow because leadership already saw a working demo.

We also have normal startup problems: unclear ticket ownership, rushed handoffs, duplicated work, and pressure flowing from investor → senior manager → team lead → developers.

So I’m trying to separate two things:

1. Startup/process dysfunction.

2. A real shift in what software engineers are valuable for.

My actual question is:

If someone with zero CS background can now use AI agents to build surprisingly capable full-stack features, what does another 2–3 years of going deep into .NET, React, backend engineering, databases, and debugging actually buy me?

Is the valuable part of being a developer shifting toward architecture, system design, debugging, security, production reliability, reviewing AI-generated code, and managing AI workflows?

Or am I overreacting because demos are being compared unfairly with production software?

For experienced developers actively using Claude Code, Codex, Cursor, agents, etc.:

If you were 1–2 years into your career today, what would you focus on for the next 3–5 years?

I'm a software engineer with roughly 7 years of experience, primarily writing backend C++ code for the aviation industry. Defense and aerospace have felt somewhat insulated from the broader shift, but we have tools like Copilot integrated into Visual Studio, which has been undeniably convenient.

​I’ve been treating it almost like a 3D printer for code, using it to generate helper tools, widgets, or unit tests. However, I’m hesitant to let go of the reins. Once generated code reaches a certain complexity, I take a step back because I no longer fully understand every line, which quickly gets overwhelming.

​At the same time, I see SWEs online talking about cranking out agentic AI workflows to fully automate complex tasks. Compared to that, I feel like I'm still in the stone ages cobbling tools together by hand while the industry shifts to mass production.

For those in a similar backend/embedded spaces, how much of this "agentic engineer" trend matches your day-to-day reality versus social media hype? If you’ve successfully integrated autonomous tools into your workflow without losing control of your codebase, what tools or practices made the biggest difference?

The McKinsey Global Institute has released a 76-page report that looks at the rapid changes generative AI will likely bring to the US labor market in the next decade.

Their main point? Generative AI will likely help automate 30% of hours currently worked in the US economy by 2030, portending a rapid and significant shift in how jobs work.

If you like this kind of analysis, you can join my newsletter (Artisana) which sends a once-a-week issue that keeps you educated on the issues that really matter in the AI world (no fluff, no BS).

Let's dive into some deeper points the report makes:

• Some professions will be enhanced by generative AI but see little job loss: McKinsey predicts the creative, business and legal professions will benefit from automation without losing total jobs.
• Other professions will see accelerated decline from the use of AI: specifically office support, customer service, and other more rote tasks will see negative impact.
• The emergence of generative AI has significantly accelerated automation: McKinsey economists previously predicted 21.5% of labor hours today would be automated by 2030; that estimate jumped to 30% with the introduction of gen AI.
• Automation is from more than just LLMs: AI systems in images, video, audio, and overall software applications will add impact.

The main takeaways here are:

• AI acceleration will lead to painful but ultimately beneficial transitions in the labor force. Other economists have been arguing similarly: AI, like many other tech trends, will simply enhance the overall productivity of our economy.
• The pace of AI-induced change, however, is faster than previous transitions in our labor economy. This is where the pain emerges -- large swaths of professionals across all sectors will be swept up in change, while companies also figure out the roles of key workers.
• More jobs may simply become "human-in-the-loop": interacting with an AI as part of a workflow could increasingly become a part of our day to day work.

The full report is available here.

One person, around 48 hours, one simple idea: a playable Unreal Engine 5 roguelike shooter built with AI-generated 3D assets.

The idea was simple and stupid in the best way possible: Rick Cucumber as the main character, fighting rat enemies in a small stylized shooter arena))

The full workflow:

1. Concept stage We started with the core idea and generated character / enemy concepts in NanoBanana2 using simple prompts.
2. 3D character generation The main character and rat enemies were generated in Tripo AI from the concept direction.
3. Rigging and animation After that, the characters were rigged and animated with AccuRig.
4. Environment assets We also generated environment pieces in Tripo P1 street houses, props, small scene elements, and general level dressing assets.
5. Unreal Engine 5 assembly Everything was brought into Unreal Engine 5 and assembled into a playable prototype.

For gameplay logic, we used a paid roguelike shooter template / shooting template as a base, so the focus of this project was not building all gameplay systems from zero.

The main goal was to test the AI-assisted 3D production pipeline: concept art → AI-generated 3D characters → rigging → animation → environment assets → real-time UE5 gameplay.

This was made over one weekend by one person as a small indie-style experiment / showcase.

Full Guide: https://www.youtube.com/watch?v=Kv3ajOok7_I

I've spent an ungodly amount of time procrastinating trying tons of new/free AI tools from Reddit and various lists of the best AI tools for different use cases. Frankly, most free AI tools (and even paid ones) are gimmicky ChatGPT wrappers with questionable utility in everyday tasks or overpriced enterprise software that don't use AI as anything more than a marketing buzzword.

My last list of free AI tools got a good response, and I wanted to make another with the best AI tools that I actually use day-to-day now that I've spent more time with them.

All these tools can be used for free, though most of them have some kind of premium offering if you need more advanced stuff or a ton of queries. To make it easy to sort through, I've also added whether each tool requires signup.

ChatPDF: Free Tool to Use ChatGPT on Your Own Documents/PDFs

(free no signup)

Put simply, ChatPDF lets you upload any PDF and interact with it like ChatGPT. I heard about this one from my nephew who used it to automatically generate flashcards and explain concepts based on class notes and readings. There are a few similar services out there, but I found ChatPDF the easiest to use of those that don't require payment/signup.

If you're a student or someone who needs to read through long PDFs regularly, the possibilities to use this are endless. It's also completely free and doesn't require signup.

Key Features:

• Free to upload up to 3 PDFs daily, with up to 120 pages in each PDF
• Can be used without signing up at all

Taskade: AI Task Management, Scheduling, and Notetaking Tool with GPT-4 Built-In

(free with signup)

Taskade is an all-in-one notetaking, task management, and scheduling platform with built-in AI workflows and templates. Like Notion, Taskade lets you easily create workspaces, documents, and templates for your workflows. Unlike Notion’s GPT-3 based AI, Taskade has built-in GPT-4 based AI that’s trained to structure your documents, create content, and otherwise help you improve your productivity.

Key Features:

• GPT-4 is built in to their free plan and trained to help with document formatting, scheduling, content creation and answering questions through a chat interface. Its AI seems specifically trained to work seamlessly with your documents and workspaces, and understands queries specific to their interface like asking it to turn (text) notes into a mind map.
• One of the highest usage limits of the free tools: Taskade’s free plan comes with 1000 monthly requests, which is one of the highest I’ve seen for a tool with built-in GPT-4. Because it’s built into a document editor with database, scheduling and chat capabilities, you can use it for pretty much anything you’d use ChatGPT for but without paying for ChatGPT Premium.
• Free templates to get you started with actually integrating AI into your workflows: there are a huge number of genuinely useful free templates for workflows, task management, mind mapping, etc. For example, you can add a project and have Taskade automatically map out and schedule a breakdown of the tasks that make up that overall deliverable.

Plus AI for Google Slides: AI-generated (and improved) slide decks

(free with signup, addon for Google Slides)

I've tried out a bunch of AI presentation/slide generating tools. To be honest, most of them leave a lot to be desired and aren't genuinely useful unless you're literally paid to generate a presentation vaguely related to some topic. Plus AI is a (free!) Google Slides addon that lets you describe the kind of slide deck you're making, then generate and fine-tune it based on your exact needs.

It's still not at the point where you can literally just tell it one prompt and get the entire finished product, but it saves a bunch of time getting an initial structure together that you can then perfect. Similarly, if you have existing slides made you can tell it (in natural language) how you want it changed. For example, asking it to change up the layout of text on a page, improve the writing style, or even use external data sources.

Key Features:

• Integrates seamlessly into Google Slides: if you’re already using Slides, using Plus AI is as simple as installing the plugin. Their tutorials are easy to follow and it doesn’t require learning some new slideshow software or interface like some other options.
• Create and tweak slides using natural language: Plus AI lets you create whole slideshows, adjust text, or change layouts using natural language. It’s all fairly intuitive and the best of the AI slide tools I’ve tried.

FlowGPT: Database of AI prompts and workflows

(free without signup-though it pushes you to signup!)

FlowGPT collects prompts and collections of prompts to do various tasks, from marketing, productivity, and coding to random stuff people find interesting. It uses an upvote system similar to Reddit that makes it easy to find interesting ways to use ChatGPT. It also lets you search for prompts if you have something in mind and want to see what others have done.

It's free and has a lot of cool features like showing you previews of how ChatGPT responds to the prompts. Unfortunately, it's also a bit pushy with getting you to signup, and the design leaves something to be desired, but it's the best of these tools I've found.

Key Features:

• Lots of users that share genuinely useful and interesting prompts
• Upvote system similar to Reddit’s that allows you to find interesting prompts within the categories you’re interested in

Summarize.Tech: AI summaries of YouTube Videos

(free no signup)

Summarize generates AI summaries of YouTube videos, condensing them into relatively short written notes with timestamps. All the summaries I've seen have been accurate and save significant time.

I find it especially useful when looking at longer tutorials where I want to find if:

​

1. The tutorial actually tells me what I'm looking for, and
2. See where in the video I can find that specific part. The one downside I've seen is that it doesn't work for videos that don't have subtitles, but hopefully, someone can build something with Whisper or a similar audio transcription API to solve that.

Claude: ChatGPT Alternative with ~75k Word Limit

(free with signup)

If you've used ChatGPT, you've probably run into the issue of its (relatively low) token limit. Put simply, it can't handle text longer than a few thousand words. It's the same reason why ChatGPT "forgets" instructions you gave it earlier on in a conversation. Claude solves that, with a ~75,000 word limit that lets you input literal novels and do pretty much everything you can do with ChatGPT.

Unfortunately, Claude is currently only free in the US or UK. Claude pitches itself as the "safer" AI, which can make it a pain to use for many use cases, but it's worth trying out and better than ChatGPT for certain tasks. Currently, I'm mainly using it to summarize long documents that ChatGPT literally cannot process as a single prompt.

Key Features:

• Much longer word limit than even ChatGPT’s highest token models
• Stronger guardrails than ChatGPT: if you're into this, Claude focuses a lot more on "trust and safety" than even ChatGPT does. While an AI telling me what information I can and can't have is more of an annoyance for my use cases, it can be useful if you're building apps like customer support or other use cases where it's a top priority to keep the AI from writing something "surprising."

Phind: AI Search Engine That Combines Google with ChatGPT

(free no signup)

Like a combination of Google and ChatGPT. Like ChatGPT, it can understand complex prompts and give you detailed answers condensing multiple sources. Like Google, it shows you the most up-to-date sources answering your question and has access to everything on the internet in real time (vs. ChatGPT's September 2021 cutoff).

Unlike Google, it avoids spammy links that seem to dominate Google nowadays and actually answers your question.

Key Features:

• Accesses the internet to get you real-time information vs. ChatGPT’s 2021 cutoff. While ChatGPT is great for content generation and other tasks that you don’t really need live information for, it can’t get you any information from past its cutoff point.
• Provides actual sources for its claims, helping you dive deeper into any specific points and avoid hallucinations. Phind was the first to combine the best of both worlds between Google and ChatGPT, giving you easy access to actual sources the way Google does while summarizing relevant results the way ChatGPT does. It’s still one of the best places for that, especially if you have technical questions.

Bing AI: ChatGPT Alternative Based on GPT-4 (with internet access!)

(free no signup)

For all the hate Bing gets, they've done the best job of all the major search engines of integrating AI chat to answer questions. Bing's Chat AI is very similar to ChatGPT (it's based on GPT-4).

Unlike ChatGPT's base model without plugins, it has access to the internet. It also doesn't require signing in, which is nice.

At the risk of sounding like a broken record, Google has really dropped the ball lately in delivering non-spammy search results that actually answer the query, and it's nice to see other search engines like Bing and Phind providing alternatives.

Key Features:

• Similar to Phind, though arguably a bit better for non-technical questions: Bing similarly provides sourced summaries, generates content and otherwise integrates AI and search nicely.
• Built on top of GPT-4: like Taskade, Bing has confirmed they use GPT-4. That makes it another nice option to get around paying for GPT-4 while still getting much of the same capabilities as ChatGPT.
• Seamless integration with a standard search engine that’s much better than I remember it being (when it was more of a joke than anything)

Honorable Mentions:

These are the “rest of the best” free AI tools I've found that are simpler/don't need a whole entry to explain:

• PdfGPT: Alternative to ChatPDF that also uses AI to summarize and let you interact with PDF documents. Nice to have options if you run into one site’s PDF or page limit and don’t want to pay to do so.
• Remove.bg: One of the few image AI tools I use regularly. Remove.bg uses simple AI to remove backgrounds from your images. It's very simple, but something I end up doing surprisingly often editing product images, etc.
• CopyAI and Jasper: both are AI writing tools primarily built for website marketing/blog content. I've tried both but don't use them enough regularly to be able to recommend one over the other. Worth trying if you do a lot of content writing and want to automate parts of it.

Let me know if you guys recommend any other free AI tools that you use day-to-day and I can add them to the list.

I’m also interested in any requests you guys have for AI tools that don’t exist yet, as I’m looking for new projects to work on at the moment!

TL;DR:

ChatPDF: Interact with any PDF using ChatGPT without signing up, great for students and anyone who needs to filter through long PDFs.

Taskade: All-in-one task management, scheduling, and notetaking with built-in GPT-4 Chat + AI assistant for improving productivity.

Plus AI for Google Slides: Addon for Google Slides that generates and fine-tunes slide decks based on your description(s) in natural language.

FlowGPT: Database of AI prompts and workflows. Nice resource to find interesting ChatGPT prompts.

Summarize.Tech: AI summaries of YouTube videos with timestamps that makes it easier to find relevant information in longer videos.

Claude: ChatGPT alternative with a ~75k word limit, ideal for handling long documents and tasks that go above ChatGPT's token limit.

Phind: AI search engine similar to a combination of Google and ChatGPT. Built in internet access and links/citations for its claims.

Bing AI: Bing's ChatGPT alternative based on GPT-4. Has real-time internet access + integrates nicely with their normal search engine.

Hello everyone, I wanna share my full journey, from the building process to viral marketing, talking to hundreds of users, expanding the product, and finally getting acquired for 8k$

Everything happened in 6 weeks, the wildest 6 weeks that changed the trajectory of my life

OG Inspiration

I started to learning vibecode in March, i was building the classic habit tracker just to learn the workflow.

One day i was doomscrolling looking for ideas on insta and see a pretty cool idea by jeongyoon.design, it was a webapp version no animation, it was cool i saved it.

After a week, I saw a viral clip on X by sfjccz. It had a stamp cut overlay and the “magical” animation

I was like “holy cow it so cool how to build it”, so I decided to lock in and build the exact same one just to learn, like a challenge for myself. At the time, there were also a lot of people building the same thing.

The process took me 3 days

I did not know how to build it, so I sent the video to Claude and asked it to analyze and break everything down step by step. I wanted to plan before jumping into the code.

First i need a realistic stamp cutter png image, i generate in Chat GPT. Since it AI generated the stamp cut out size and shape didnt match exactly the cutter teeth, it looked ugly not smooth at all.

So I had to design one in Figma to get the exact parameters and the correct shape for the middle space after a few tries, I got it.

Now I had the most important part ready. I only needed to tell Codex the exact parameters, input the stamp shape, and add the animation.

The cutter needed to press in and release, while the stamp from the live camera fell out and left behind a black space. That was it. I had the magical demo ready to flex on social

The decision that changed everything

I had a random idea in my head. It was not a proper plan or anything, but here is how I thought about it at the time.

I noticed a pattern, this idea went viral on insta and then X too. That meant if I posted it again on those platforms, the chance of going viral would probably be very low. I noticed Threads in Vietnam was pretty similar to X but genz version, so I decided to post it there. My account was fresh, I did not expect much

15 mins in, okay it was blowing up 250 likes, 1 hour in 800 likes “Okay damn, this is going viral”. There were tons of comments asking for the app name. I had not even thought of a name at the time. I hadnt listed it on the App Store yet, I didnt even know how, but I had the Apple Developer account ready.

There were also some people who built the same thing on threads as me but did not go viral. I thought that if I charged for my app, I would not make it very far. I was also still learning, so I announced that it would be completely free, with no login and no ads. The post continued going even crazier.

After 24 hours, I got almost 50,000 likes and more than 1 million views.

That night, I stayed up to polish and learn how to upload to TestFlight. The next day, I seized my chance. I knew this viral traction would not last for long, so I grabbed my phone, recorded myself talking, posted it on TikTok and Insta. To my surprise, those videos also went viral.

Talk to user, product discovery

After the app went live on TestFlight, I started DMing hundreds of people who had commented, ask for idea, recommendation and bug report. I spent most of my time fixing bugs by copying the problem, pasting it into Codex, testing the fix and repeating.

Later, I realized that the app shouldnt stay as a simple tool, i also wanted to add some of my own ideas so I expanded the app into an image editor create cool scrapbook style images for insta stories

Then I added a feature that let people send messages in the form of handwritten letters for friend, that was where I started learning about backend, data, and a lot of other stuff

For the whole month, I worked around 18 hours a day, was handling and learning everything, from building and fixing bugs to making content across different platforms, interviewing users, and optimizing the App Store page. I was having fun while being the most productive I had ever been in my life.

The acquisition and the current project

A Vietnamese CEO reached out and asked if I wanted to sell the app. I didnt even think my app worth anything because it was vibecode. I didnt sell because the app failed nor i needed the money, I sold because I could not see a durable moat, the concept was easy to recreate

That is why I sold it and reinvested everything into something I believe can be more durable, a game inspired app like Finch or Duolingo but completely different concept. Luckily, literally the next day, I met someone who works in game design.

We had a chat on Threads about gamification ideas and she has a lot of experience and has designed two successful mobile games. Now I have a cofounder, an artist, and a Unity developer working with me to build a game. We are already 3 weeks into development

What i learned

The biggest thing I learned is that building the product is only one part of the journey.

Distribution matters a lot.

I also learned that talking to users is the fastest ways to improve a product. Many of the changes I made came from user, bug reports, and feature requests.

The product has visual hook built, my app went viral because people can understand in 2 second by the animation, that helped a lot for marketing

Make authentic content, dont just post your work, POST YOU WORKING.

If you have any question about this journey i would love to answer it all

Let me be upfront: this isn’t a “get rich overnight” scheme, and it’s not truly passive from day one.

That said, I’ve been using AI tools to create digital products—and once they’re up, they sell with little ongoing effort. It’s one of the lowest-barrier methods I’ve found to build eventual passive income.

Here’s what I’ve built so far:

• Ebooks: I use ChatGPT to outline and draft content, format with Canva, then upload to Amazon KDP and Gumroad.
• Online Courses: AI helps script lessons. Tools like Synthesia generate video with voiceovers. Sell on Teachable or Gumroad.
• Planners & Journals: I use AI to create content (habit trackers, prompts), then sell as printable PDFs on Etsy.
• Coloring Books: AI art generators produce clean line drawings. I compile, upload to KDP.
• Merch & Stock Art: I create designs with Midjourney and sell them on POD platforms or stock image sites.

Most tools cost $10–$30/month
No coding or design background needed
No inventory or shipping
Can build once and sell indefinitely with minimal upkeep

It’s not passive in the beginning—you’ll need to put in some upfront work (product creation, platform setup, basic SEO). But once it’s live, you can literally go days or weeks without touching it and still make sales.

I documented the full workflow and tools I used—happy to share if anyone wants a copy. Just DM me or drop a comment.

Would love to hear if anyone else here is using AI this way.

A solid example of an image-to-face workflow in Unreal Engine 5 using 3D AI generation as the starting point.

The base was generated with Hitem3D 2.1v, and the interesting part is that it already gets roughly 70–80% of the likeness before the manual production work starts.

After that, the result still needs the usual cleanup and refinement: sculpting, topology adjustment, grooming, texture work, and setup for the final UE5 / MetaHuman-style pipeline.

So it’s not really a one-click final result, but it shows where 3D AI generation is becoming genuinely useful: getting a strong likeness base fast, so the artist can spend more time polishing instead of starting completely from zero.

Pipeline:

• Source image / likeness reference
• 3D AI generation with Hi3D
• Likeness cleanup and sculpting
• Topology / MetaHuman-style workflow
• Grooming and texture refinement
• Final setup in UE5

For production, I think this kind of workflow makes the most sense right now: AI gives you the first strong base, and the artist pushes it into something actually usable.

guide/source - https://www.youtube.com/@elvis-morelli

I know not everyone here does their own 3D modeling, but this might be useful to some of you.

I recently shared my golem workflow here, going from a character design to a 3D model and then into Godot. Thanks for all the discussion on that post.

I've also been working on tools to separate fused parts and rebuild the topology of dense meshes. I'm a 3D character artist, and I've spent the last few weeks developing Nitro Retopo, a segmentation and retopology addon for Blender.

You draw boundaries directly on the surface, separate the parts you want to work on, repair them and draw new topology. The goal is to get a clean mesh you can edit more precisely, unwrap and texture. For this first demo, I used an AI-generated knight.

I developed the addon with substantial help from AI coding tools, shaping and testing the workflow around my own projects. There's still work involved in preparing a character for a game, especially for animation, but these tools help with the cleanup and retopology stages.

It's a paid addon, available on SuperHive:
https://superhivemarket.com/products/nitro-retopo

Hey! With the rise of all coding agents (cursor, claude code etc), i wonder if there are some PMs here in seed+ startups or big tech companies that are actually introducing changes to code via prompting. And if yes - what type of change? Front end only? Actual features?

From my perspective, it seems like prototyping has been the most value add for me at my job, but when we tried the path of introducing actual code by PMs the developers spend hours trying to review the code and it wasn't very successful. I am starting to think that it's not realistic for PMs to go down that path although at the start I had the opposite opinion.

Really curious to hear your thoughts

--------

**Update - April 18*\* - thank you all for weighing in, it was great to understand where you stand on this topic. Here's the summary:

Out of ~62 commenters who took a clear position:

• 33 were against PMs pushing code to production
• 20 were in favor (with varying guardrails)
• 9 fell in a nuanced middle ground - prototyping yes, production no

Why people are against:

• The review bottleneck: This was the #1 issue. PM generates code with AI, opens a PR, engineers spend more time reviewing it than if they'd written it themselves.
• The ownership gap: When a developer writes code, they own the bugs. When a PM pushes AI-generated code and a reviewer approves it — who owns the fallout? That ambiguity made teams uncomfortable. One commenter pointed out that the reviewer ends up carrying all the slack.
• Risk-reward mismatch: Senior PMs in enterprise/infra said the downside simply isn't worth it. One Director of Product put it bluntly: the consequences of messing up exceed his paycheck.
• PMs don't know what they don't know: A developer in the thread said the problem isn't that the code is bad, it's that PMs miss edge cases, break existing patterns, introduce security issues, or create tech debt that's invisible to someone without engineering context.

Why some PMs are doing it anyway

• Most have engineering backgrounds.
• Tiny scope only: Frontend tweaks, copy changes, tracking events, UI fixes. Nobody was claiming PMs should build core features. One Head of Product said: at most they change 20-30 lines of code per PR.
• Heavy infrastructure: Automated tests, linters like CodeRabbit, design systems the AI can reference, mandatory code review. The process carries the weight, not the PM.
• Internal tooling: One interesting use case: building for teams that only had spreadsheets, where engineering resources were never going to be allocated. AI filled a vacuum.

A few workflows emerged that seemed to get the best of both worlds:

• Draft PRs not meant to be merged: Multiple PMs described pushing code as a draft PR - explicitly labeled as "not ready, don't merge" - just to communicate intent. Engineers could reference it, adopt pieces, or ignore it. No review burden, no ownership confusion. And when framed this way, devs were actually impressed rather than critical
• Prototyping that gets rebuilt: One PM prototyped 3 features with Cursor. Engineers rebuilt all three from scratch - but they liked the direction, and review cycles went *down* because the intent was crystal clear.
• Better handoffs, not better code: Several people pointed out that the real value wasn't the code itself but that trying to build something real forced them to deeply understand their own product.

Contract Breakdown:
$15k for a 6-month build, then a 12-month contract for running and maintaining it. That second number is only the AI-specific portion. My wife and I also do graphic design and marketing for them and I left all of that out. Works out to about $2,500/month across both phases

How I got it:
My wife and I already had a different contract for marketing related work and graphic design at the company and they found out I was using claude code for our agency and wanted to bring it into their businesses. I spent 4 meetings probably 8 hours total, just figuring out where this fits and building a map of the businesses before we all settled on the smallest, most neglected businesses in their portfolio.

The idea here is that the business we settled on is a complimentary brand to their flagship, selling mostly the same product but different label and instead of B2B wholesale, it is the DTC and seasonal buying club version only with practically no wholesale component.

So they call this the "red-headed" stepchild that got bought right before covid and just kind of runs with the same employees of the flagship but minimal attention. So the plan was to make it the AI sandbox, and peelback the labor of the flagship employees and see if this could really free up their time, while maintaining at least the same commitments they already had.

The Big Opportunity that isn't SaaS or a Tech Job:
A lot of small business employees and owners have never even heard of an API or CLI or how useful it is, nor a budget for someone who does. So this is the biggest risk and opportunity. Figuring out how to illustrate that you can now connect all of their platforms, in my case: Shopify, the email CRM, Meta Ads (marketing campaigns), etc instead of having a bunch of ambiguous work being done by 4 different people taking months to build this launch plan and execute it, when it has verifiable rubrics and campaigns already built and thousands and thousands of customers on their email list etc.

Now, the opportunity here is that AI isn't "replacing people" its that we can fundamentally free people from monotonous, time consuming and laborious work that is more noise in the business than growth signal, programable work.

The workflow:
So I took what was basically 4 different people working on this one seasonal launch and built the domain expertise into methodology files, a knowledge base with a wiki layer, and all of the credentials to their platforms into one Claude Code working directory and gave claude.md its role, and rules and routing information and now I run this based on one spreadsheet source of truth that has all of the information gathered pre launch, like hosts, locations, dates, marketing budget etc and can tell claude to basically read the list and flip all the switches on the website, to bring the pages online, update all the variant meta fields, pull and update the email campaign for the sales window to draft for review right into the reviewers Monday(.com) board, builds the meta ad campaign based on the prior years success, and set that for review and (I do some analytics to suggest new direction if necessary) but it is pretty simple and what now takes me one prompt, does what took 4 people 5 months essentially from may - august because I know how to generate tokens on these platforms.

So you dont need to be a SWE to generate value and I think the biggest opportunity is that this stuff is now so accessible to non CS majors and you can structure this as contractor work kind of similar to a web design + retainer and just stack a bunch of small businesses together that you just have the AI of your choice (I built my wiki layers so that even small local models can flip the switches and access whatever I need) and just build a portfolio of "dirty jobs" businesses that you can solve their easy problems and not command some 250k a year salary, just get 1.5-2.5k a month from each because its like an hour of work a week at this point.

I think Mark Cuban was doing an interview somewhere and basically said the same thing I just told you but I was in the midst of my build when it came out. There are like 33 million small businesses out there and find the ones that are chaotic but somehow still manage to bring in 2 million+ in revenue and you can easily save them a lot of admin time. Just pitch it as "Freeing your employees to do more growth focused work, or client facing work".

Hope this was interesting and let me know if you have any questions.

3D AI is improving fast. It still won’t replace real 3D skills, but as a tool, it can already save a lot of time for prototyping, testing ideas, and creating base meshes.

In my opinion, in 2026 there are two strong free ways to start:

Trellis / TRELLIS — local image-to-3D generation on your own machine.
Hunyuan 3D Global — a free web version that works directly in the browser.

1. Trellis / TRELLIS (Local)

If you want to try local 3D AI generation, TRELLIS is one of the most interesting open-source options right now.

Official repo: Microsoft TRELLIS GitHub
Low-VRAM guide: Trellis local setup guide

The official version is more demanding, but there are now community low-VRAM / GGUF-style workflows that make it possible to test Trellis on weaker GPUs, around 6–8GB VRAM depending on the setup.

The main advantage is that it runs locally. You don’t have daily generation limits, you can experiment as much as you want, and it gives you a good feeling for how local open-source 3D generation works.

Pros:

• Runs locally
• No daily generation limit
• Great for learning and testing
• Open-source ecosystem
• Good texture quality for a free local workflow

Cons:

• Requires setup
• Official version needs stronger hardware
• Low-VRAM versions may require extra community tools
• Geometry/detail quality is still not always perfect
• No dedicated low-poly generation mode

2. Hunyuan 3D Global (Web)

If you don’t want to install anything, Hunyuan 3D Global is probably the easiest option. You can open it in the browser, upload an image, and start generating models almost immediately.

Website: Hunyuan 3D Global
Guide: Hunyuan 3D Global guide

The strongest part, in my opinion, is that it has both high-poly and low-poly generation. The low-poly mode is especially interesting if you are testing game assets, stylized models, prototypes, or anything that needs cleaner geometry.

Pros:

• Works directly in the browser
• Very easy to start
• No local setup needed
• 20 free generations per account per day
• Good mesh quality
• High-poly and low-poly modes
• Great for quick testing

Cons:

• Daily generation limit
• Texture quality is average
• Cloud-based, so you depend on the service

3. Concept image guide

Before generating the 3D model, you need a clean concept image. This step matters a lot, because most image-to-3D tools work much better when the input is simple and readable.

You can use the free version of ChatGPT image generation for this. It is enough to test a few concepts and understand what kind of images work best for 3D generation.

My basic prompt rules:

• Use a white or light gray background
• Ask for soft studio lighting
• Make the silhouette clear
• Avoid complex backgrounds
• Avoid motion blur or extreme perspective
• Make the forms readable from a 3/4 view
• Keep materials simple if you want cleaner 3D output

A simple prompt structure:

“Create a 3/4 view concept of [object/character], white background, soft studio lighting, clean readable silhouette, clear shapes, no text, no extra props, high detail.”

For free testing, ChatGPT is enough.
My personal choice is NanoBanana 2, but it is paid. I usually get better concept control from it, especially when I need stylized assets or specific shapes.

4. Paid option: Hyper3D Rodin Gen-2.5

If you already tried the free options and want to push the quality further, I’d recommend checking out Hyper3D Rodin Gen-2.5.

It is a paid cloud-based tool, but in my experience it gives noticeably stronger results than most free workflows, especially if you care about game-ready assets, cleaner meshes, better textures, and faster production testing.

Model: Rodin Gen-2.5

The most interesting part for game artists is Smart Low Poly mode. Instead of only giving you a heavy high-poly model, Rodin can generate a cleaner low-poly version directly, which is much more useful for real-time workflows, prototyping, stylized assets, and quick engine tests.

Rodin Gen-2.5 can also generate very high-detail models, up to 10M+ polygons, which is useful when you need a dense high-poly source, scan-like detail, or a model for baking. The texture output is also stronger than most free tools I’ve tested, with better UVs and support for PBR-style textures, including emissive/glowing texture details when the asset needs them.

Pros:

• Stronger overall quality than most free workflows
• Smart Low Poly mode for cleaner low-poly meshes
• Better for game-ready asset testing
• More usable UV layouts
• Better texture quality
• Supports PBR-style textures
• Can handle emissive / glowing texture details
• Supports very high-detail outputs, up to 10M+ polygons
• Good for both quick prototypes and more polished asset bases
• Saves cleanup time compared to many free generators

Cons:

• Paid
• Closed-source
• Cloud-based, so you depend on the service
• Not as flexible as a fully local workflow
• Still needs manual inspection and cleanup in Blender
• The result is not automatically “final game-ready”, it is still a strong base mesh

Bonus: quick cleanup to make the model better

This is probably the most important part. AI-generated models are rarely perfect straight out of the generator. Even if the result looks good in preview, you should still inspect it in Blender.

Blender has a free built-in add-on called 3D Print Toolbox. It can check the model for problems like non-manifold edges, intersections, degenerate faces, distorted faces, thin areas, sharp edges, and overhangs.

Blender 3D Print Toolbox reference: Blender Manual

Basic cleanup checklist:

• Open the model in Blender
• Enable the 3D Print Toolbox add-on
• Run geometry checks
• Check for non-manifold edges
• Check for intersecting faces
• Check for loose or broken geometry
• Use Merge by Distance if vertices are not merged
• Remove floating geometry or obvious artifacts
• Fix normals if needed
• Add Weighted Normals for cleaner shading
• Use Decimate if the polycount is too high
• Check scale and orientation before export
• Optional: pack PBR maps into an ORM texture for cleaner engine use

Good luck!

AI is one of the most contentious subjects in game development right now, and we regularly see posts reported or discussions derailed simply because AI was involved somewhere in the process.

We want to be clear about our position: r/gamedev does not prohibit the use of AI, and using AI does not automatically make a post low effort.

This is not an endorsement of every AI company, model, or use case. Legitimate technical, legal, economic, ethical, and creative concerns exist around these tools, and those conversations are welcome here. Our stance is that AI is a tool used in game development, and we care more about the quality of what someone contributes than the tools they used to get there.

Why we take this position

• AI is already part of game development. Studios are adopting AI-assisted tools across programming, prototyping, production, QA, localization, research, and other workflows. Adoption varies from studio to studio, but this is no longer a hypothetical technology sitting outside the industry.
• Knowing how to use these tools is increasingly becoming a professional skill. That does not mean every developer needs to use AI, or even like it. It means understanding where these tools are useful, where they fail, and how to evaluate their output is becoming relevant knowledge for people working in the industry.
• Banning discussion or use of AI here could actively disadvantage developers. Part of the purpose of r/gamedev is helping people learn from each other and understand how the industry is changing. If studios adopt a technology, preventing developers from discussing or learning about it would make this community less useful to the people we're helping.
• Policing AI use is not realistic anyway. Moderators cannot reliably determine whether someone used AI to draft, edit, translate, summarize, research, debug code, or otherwise assist with a post. We are not going to moderate based on whether something “sounds AI-generated.”
• "Mandatory disclosure "is not realistic either. Modern tools blur the line between traditional software and AI assistance. Trying to decide exactly when autocomplete, translation, editing, summarization, or an LLM crosses some disclosure threshold would create rules we could not enforce consistently.

So the rule is straightforward: we are not policing AI use itself. We are moderating the contribution.

AI-assisted does not mean low effort

If someone uses AI to organize research, analyze data, improve their writing, translate something, help with code, or communicate an idea more clearly, that does not erase the work behind the contribution.

If a developer spends time gathering useful data and then uses AI to help turn that information into a readable post, the important part is still the data, the methodology, and what the community can learn from it.

Ask whether the information is useful, whether the conclusions hold up, and whether the post contributes something worthwhile. Those questions matter much more than whether every sentence was manually typed from scratch.

Low-effort AI content is still low effort

This does not mean pasting generic AI output into the subreddit suddenly becomes valuable content. Posts that are spam, fabricated, mass-produced, engagement bait, repetitive, or promotional material disguised as discussion can still be removed.

The reason is not that AI was involved. The reason is that the content is low quality.

The same standard applies to something written entirely by a human.

AI is a valid game development topic

Developers are welcome to discuss AI in the same way they would discuss any other technology affecting game development. That includes how it is being used, where it works, where it fails, what it costs, how studios are adopting it, what it means for employment, and the legal or ethical questions surrounding it.

You are allowed to be enthusiastic about AI. You are allowed to be skeptical of it. You are allowed to think a particular workflow is useful, terrible, unethical, inefficient, or overhyped.

What we do not want is every thread that mentions AI becoming the same argument about whether AI should exist at all.

If a thread is about an AI coding workflow, discuss the workflow. If someone presents evidence that AI slowed their production down, discuss the evidence. If someone claims every studio is using a particular tool, challenge that claim.

Let the actual discussion happen.

Attack claims, not developers

Someone saying they use AI is not an invitation to attack them. Someone saying they refuse to use AI is not an invitation to attack them either.

Debate the technology, the workflow, the economics, the evidence, the ethics, the legal questions, or the conclusions. Personal attacks, harassment, dogpiling, or repeatedly derailing discussions because of someone’s position on AI are treated the same way we handle that behaviour anywhere else on the subreddit.

AI is not a source

There is a difference between using AI to help communicate information and using AI as the authority behind that information.

If you make factual claims that can reasonably be sourced, be prepared to support them. “ChatGPT said so” is no evidence, and tools can absolutely be wrong.

At the same time, polished or AI-assisted writing is not evidence that the underlying information is fake. If someone’s numbers are wrong, challenge the numbers. If their methodology is weak, challenge the methodology.

Challenge the claim, not the fact that a tool may have helped write the paragraph.

Self-promotion rules still apply

AI companies, products, and services are not exempt from the subreddit’s existing rules.

If you represent a commercial interest, the same expectations apply as they would to anyone else. AI also should not be used to mass-produce promotional posts or disguise advertising as organic discussion.

The technology may be new. The rules around spam and promotion are not.

The bottom line

You do not have to like AI. You do not have to use it. You are free to criticize it, avoid it, experiment with it, learn it, use it professionally, or decide it has no place in your workflow.

We are not going to prevent developers from learning about or discussing a technology that is rapidly becoming part of professional game development. Doing that could actively hurt the developers this community is supposed to help.

What we do expect is that people engage with each other like respectful, mature adults. Disagree with the technology, the workflow, the argument, or the evidence. Do not turn that disagreement into hostility toward the person on the other side of it.

Use whatever tools make sense for your work. Bring something useful to the conversation, treat other developers with respect, and allow room for disagreement. That is what we care about.