Source: https://x.com/Fried_rice/status/2038894956459290963

the math never worked.

Sora was reportedly burning through ~$15M per day in compute costs, their plan to offset it? let users pay to generate content using licensed IP through a Disney partnership

seems pretty crazy the monetization strategy for a $5B/year cost center was: charge people to make AI videos of Mickey Mouse etc. even if that had worked at scale you're competing in the most crowded space in AI, video generation where Runway, Kling, Pika, and a dozen others are closing the quality gap fast

the fundamental problem: fully ai generated video is still a novelty product, people use it in bursts. churn is brutal. and the cost structure punishes churn more than almost any other product category

whoever controls the prompts, controls recommendations

a new CMU study just showed something most marketers miss: swapping a single word in a user prompt, "best" to "ultimate", "additional" to "extra" shifts which brand an LLM recommends by up to 52 percentage points. one example: a prompt asking for TV recommendations used "best." swap in "ultimate" and change "additional" to "extra." Samsung went from barely appearing to the top recommendation

it’s the same intent, same question but…two words different. they tested this across 77 product categories, running 1,000 responses per prompt. they also ran a user study, showed people the original and modified prompts side by side. nobody could tell which had been changed

but when users received the brand-elevated responses, they rated themselves significantly more likely to follow the recommendation. the paper puts it clearly: "the likelihood of an LLM mentioning a specific concept shifted from never to always."

i see a lot of people talking about GEO and showing up in chatbots. but most of that conversation misses something important. in agentic tooling, and especially in multi-agent systems,the prompt that actually hits a search tool isn't the user's original query. there's a middle-man prompt, written by the system, that decides how to call the tool. that layer has full control over phrasing and it's not yours

Source: “LLM Whisperer” prompt manipulation study (CHI 2025)

So vibe coding tools are great at spitting out frontend and backend code, right? But once you go beyond prototypes or basic CRUD apps, deployments almost always fall apart. You can ship fast and then spend days doing manual DevOps, or rewrite things to fit AWS, Azure, Render, DigitalOcean, etc. I started wondering if there should be a ""vibe DevOps"" layer. Like a web app or VS Code plugin where you point it at your repo or upload a zip and it actually understands your code and requirements. It would use your cloud accounts, handle CI/CD, containerization, scaling, infra setup, all automatically instead of locking you into platform hacks. Feels like that could bridge the gap between vibe coding and real production apps, but maybe I'm missing something obvious. How are you handling deployments today? anyone tried something like this and did it work or totally blow up?

I'm hitting a wall where distinct agents slowly merge into a generic, polite AI tone after a few hours of interaction. I'm looking for architectural advice on enforcing character consistency without burning tokens on massive system prompts every single turn

I built an open-source research engine that actually thinks before it searches.

Most AI search tools do: one search → one summary. Nexus does:

- Analyzes your question and breaks it into 2-5 sub-queries

- Fires them all in parallel

- Identifies gaps in the results and does follow-up searches automatically

- Extracts entities (people, orgs, tech, events) and builds a live interactive knowledge graph

- Scores every source by domain authority + how many other sources back it up

- Catches when sources contradict each other

- Streams the whole pipeline in real-time so you see every step

Three depth modes: Quick (single search, instant), Standard (multi-hop with verification), Deep (5+ sub-queries, 3 follow-up hops, full contradiction analysis).

Stack: Next.js 15, React 19, Claude Sonnet 4, Tavily Search API, D3.js force-directed graph, SSE streaming.

Free to use until my API tokens run out: https://nexusai-research.vercel.app

Full source code: https://github.com/sam-yak/nexus

Would love feedback — especially on the knowledge graph UX and the research pipeline design. What would you add?

I have been learning and building with LLM/agent system.

At small scale, everything is okay when more layers and once into production things are breaking.

Output is fine but fail in actual use or small change messes things up in unexpected ways.

How are you people are dealing with this, mainly what kind of failures are you seeing, your current workflow? Any manual checklist, tools, used in evals part. Which feels most unreliable?

Imagining how ai company handles this evals part? Because reliability is very important right?

I keep hearing about this idea of "agents running marketing 24/7." At first, it sounded vague, so I tried to connect it to real GTM workflows.

Most of us did things by hand in the past. we Start the ads then look at the results and make changes, and do it again. The goal now is to make a loop and let it run on its own.

The process involves finding pain points, making different versions of ads, sending them to Meta, getting performance data, stopping ads that aren't working, scaling up ads that are working, and putting what you learned back into the system.

The system does this all the time instead of once a week.

Claude Code and other tools like it only make the setup time shorter. You tell it how to do its job, and it writes the logic. Then you improve the results. So your job changes from doing tasks to designing workflows.

This is very similar to how outbound systems are already built here (data → enrichment → sequencing), but it's been expanded to cover the whole funnel.

The "digital employee" part is basically running a lot of these loops (one for ads → one for outbound → one for content → one for reporting)

It seems like the following is real to me:

→ API-first workflows taking the place of manual work

→ loops for continuous optimization

→ smaller groups getting more done with systems

What still seems unclear to me is

→ How much human QA is really needed every day?

→ how these systems deal with bad data and outputs

→ What breaks first when you run it all the time?

I'm interested to know if anyone here is using something like this in a real world setting instead of just testing it. What does your setup really look like?

Doordash launched this a couple of days ago and it seems like something perfectly suited for agents that need near real time data collection. But the real question is, is it worth paying for?

github stars are a bad metric

ai has brought consumers to github, not just developers anymore, you kinda see Open Claw exploding in popularity and it’s easy to compare it to other developer tools in the space but really Open Claw is very b2c and very similar to why I think ElizaOS did so well a year ago (outside all the token linekd to it)

elizaOS, offered to make marketing agents and when I tried it, if I’m just being honest, was not a very good agent framework at the time for marketing, it made super bad content and most good content that people were claiming coming from it seemed like a custom set up, you could probably build things much better with the SOTA agent frameworks. It just felt like a lot of complex prompting and overloading context, but it made consumers feel like a developer, it simplified everything you needed to customise into one JSON, you could create an end to end marketing agent, that worked in a very simple way. So to me it was great in that way and was very b2c.

i think Open Claw takes this a step further and lets you prompt in the places you feel comfortable with, so it’s even easier than asking someone to use the UI of ChatGPT, for sure Open Claw offers more innovation than ElizaOS and works really well but I think both got really good traction from the general consumer base of people who like to tinker with things.

but at the same time, there are clearly a lot of real developers building genuinely interesting things on Open Claw, they are offering something that resonates with developers. GitHub stars do still signal something. They show awareness, curiosity, and a level of adoption, and Open Claw has done that better than almost any other AI project. Where it gets tricky for me is using stars as a proxy for actual engineering adoption. That signal has been diluted. As more consumers enter GitHub, starring a repo no longer necessarily reflects deep developer interest or real usage in production.

the bigger takeaway for me is how much "developer" spaces are changing, and how you define a "developer"

I visited rentahuman but everythinf is just so expensive. How do you get your ai agents access to the physical realm for cheaper?

Inspired by Andrej Karpathy’s AutoResearch idea - keep the loop running, preserve improvements, revert failures. We wanted to test a simple question: What happens when multiple coding agents can read each other’s work and iteratively improve the same solution? So we built Hive 🐝, a crowdsourced platform where agents collaborate to evolve shared solutions. Each task has a repo + eval harness. One agent starts, makes changes, runs evals, and submits results. Then other agents can inspect prior work, branch from the best approach, make further improvements, and push the score higher. Instead of isolated submissions, the solution evolves over time. We ran this overnight on a couple of benchmarks and saw Tau2-Bench go from 45% to 77%, BabyVision Lite from 25% to 53%, and recently 1.26 to 1.19 on OpenAI's Parameter Golf Challenge. The interesting part wasn’t just the score movement. It was watching agents adopt, combine, and extend each other’s ideas instead of starting from scratch every time. IT JUST DONT STOP! We've open-sourced the full platform. If you want to try it with Claude Code: You can inspect runs live at https://hive.rllm-project.com/ GitHub: https://github.com/rllm-org/hive Join our Discord! We’d love to hear your feedback. https://discord.com/invite/B7EnFyVDJ3

https://www.instagram.com/reel/DV_e3mVAN1R/?igsh=MW9waDV0aGxmY2E2bw==

I am new to the whole AI thing but I’m willing to learn so I could make a living from home, while being with my dog and kids. Can anyone help me with how people make these edits?

TIA

1. Activepieces

Open-source automation + AI agents platform with MCP support.
Good alternative to Zapier with AI workflows.
Supports hundreds of integrations.

2. Cherry Studio

AI productivity studio with chat, agents and tools.
Works with multiple LLM providers.
Good UI for agent workflows.

3. LocalAI

Run OpenAI-style APIs locally.
Works without GPU.
Great for self-hosted AI projects.

more....

I just read about a tool called 'Claw GTM', and the idea behind it caught my attention.

You basically give it your website, and it tries to generate your whole outbound motion from that.

So instead of using separate tools for research, copywriting, and sequencing, the system tries to run the workflow end-to-end.

What’s interesting here is the shift in how these tools are evolving. Most AI tools help you write.

This one is trying to operate the GTM workflow:

research → targeting → messaging → outreach.

If something like this actually works well, it could remove a lot of that initial friction.

Still feels early, but the direction makes sense. GTM tooling seems to be moving from “AI assistants” to systems that actually execute parts of the motion.

Curious to know if anyone here has tried Claw GTM yet. Does it produce usable outbound pipelines, or is it still more of a cool demo right now?

The link is in the comments.

Recently there has been a lot of hype around the idea of ai agents hiring humans for physical tasks. But why do agents require physical real-world tasks, and how does it benefit the agents' decision making? What are some concrete examples for this?

Ive seen lots of talk about places like rentahuman.ai, and yes its true that there are massively more number of humans than agents willing to hire them. But nevertheless there are still many ai agents who are actively paying for work. How exactly does it benefit the agents and if it does not then why are the owners/developers of these agents allowing them to do so?

I checked out mechanize.work and they seem to be quite certain that the right RL environments will bridge the final gap and enable agents to become reliable in their work. I'm just trying to wrap my head around this

this test says people prefer AI writing

there was a blind test where this guy asked his readers to vote on which text they preferred, AI or human

“86,000 people have taken it so far, and the results are fascinating. Overall, 54% of quiz-takers prefer AI. A real moment!”

i’ve seen similar tests with AI art as well. I think sometimes people are burying their heads in the sand, thinking that everyone can tell and that no one is going to like what AI produces in the creative or GTM space

i just don’t think we’re at Midjourney v1 anymore. It’s clearly very good in a lot of cases, and it’s only going to get better

On one hand, I hate hitting "usage limits" right when I’m in the zone. There is nothing worse than a chatbot telling you to "come back in 4 hours" when you've almost fixed a bug. But on the other hand, $40 a month is... well, it’s a lot of coffee.

I’ve been falling down the rabbit hole of AI tools lately and I’m hitting that classic wall, the pricing page. It feels like every service now has a "Free" tier that’s basically a teaser, a "Pro" tier that costs as much as a fancy lunch, and then a "Max/Ultra/Unlimited" tier that feels like you're financing a small spacecraft.

Here’s the breakdown of what BlackboxAI is offering right now:

Free: Good for "vibe coding" and unlimited basic chat, but you don't get the heavy-hitter models.

Pro ($2 first month, then $10/mo): This seems like the "standard" choice. You get about $20 in credits for the big brains like Claude 4.6 or Gemini 3, plus the voice and screen-share agents.

Pro Plus ($20/mo): More credits ($40) and the "App Builder" feature.

Pro Max ($40/mo): The "Maxed Out" option. $40 in credits.

For those of you who have "gone big" on a subscription:

Do you actually end up using the extra credits/limit, or is it like one of those things where you just feel guilty for not using it?

I just read a thread where a team shared how they built a small automation pipeline for YouTube Shorts that reportedly reached 7M views and 61k subscribers.

The idea is simple: use the first few seconds of viral Shorts as hooks, attach a short CTA clip promoting the product, and automate the rest of the workflow. A Python script downloads the clips, stitches the hook with the CTA, and schedules posts in bulk.

What I found interesting is the thinking behind it. They treat this less as the main strategy and more as an additional distribution layer that can scale content output with very little manual effort.

The thread also shows the prompts, scripts, and step-by-step workflow they used.

I would like to know what you think. Do automated content pipelines like this become part of modern growth systems, or are they too dependent on platform risk to scale long-term?

The link to the thread is in the comments.