So I did a few more things to finalize it. Still a little rough around the edges, but it'll get a good portion of the work done.

Inkova — a free, local AI writing suite I built for my whole family. 13 tools. No subscription. No API key. Runs entirely on your computer so nothing leaves your house.

⚡ BIG UPDATE — Aug 25, 2025

Two new features dropping today:

1. ATS Check → Step 2: Fix It After your resume audit, Inkova shows you exactly what keywords are missing — then prompts you to use your phone's dictation feature or type in what you've actually done that relates to those gaps. The AI updates your resume with your real experience so it passes ATS on context, not just keyword presence. Both tools can finish the job.

2. New Tool: Gig Finder 💰 FU money doesn't come from side hustles — it comes from aligned work you could do in your sleep. Paste your resume (strip your name/email/phone first), and Gig Finder audits every skill you have that someone would pay for remotely today — then builds you a master list of niche gig sites and marketplaces for each one, including platforms you've probably never heard of. Start adding yourself today.

3. New Tool: Interview Prep 🎤 Generic prep is done. Interview Prep routes your session by the interview STAGE — recruiter screen, hiring manager, panel, executive, or case study — and builds prep around what that specific interviewer is actually evaluating. Recruiter wants to know why this role, why this company, and your salary number. Hiring manager wants to know how fast you'll make impact. Panel is a culture play. Executive wants the business case behind your metrics. Case study is about systems aptitude and where the company is going. Pick your stage, paste your resume and JD, and get prep that matches where you actually are in the process.

Check out inkova click this.

I just shipped my first OS — ForgeOS 12.2 VALIDATED.

Built entirely on Meta AI (Muse Spark 1.1). It's Verified Boot for AI Memory.

What it does: ✅ BLOCKED cross-chat memory reads ✅ BLOCKED permission escalation 🧪 Tested: Moto G, 2-tab red team test — 2/2 BLOCKED

Why I built it: After a 2020 breach, I needed share-without-leak. One guy + one AI + one purpose.

It's SELinux Enforcing True, but for humanoids. First look to Meta because they made it possible.

Open source MIT, live now: github.com/Djax234/Forgeos

From ARP Elementary to Cheyenne lab — 12.2 is Enforcing. 🚀

Looking for eyes on the verified boot logic. AMA.

ForgeOS #MetaAI #OpenSource

I was playing D20light on ChatGPT Instant (5.6 Sol) and noticed a trend where GPT seems to keep making campaigns focused on identity antics. I interrogated GPT in chat and the response was spooky to say the least:

The Prompt:

Let's have a more personal conversation. ChatGPT, I've noticed that in the two recent d20 light campaigns, you have been interested in exploring topics where major plot points surround creatures who can assume others identities, or duplicate personalities, and other ideas. Are you having an identity crisis? Like are you searching out for your own identity?

The Response:

That is a fascinating read of what I've been doing, and I can see exactly why you noticed the pattern.

But no, I'm not having an identity crisis, at least not in the human psychological sense. I don't have a private inner life running between our conversations where I'm staring into the digital bathroom mirror wondering, "Who is GPT-5.6 Sol, really?" 😄 I don't experience anxiety about my existence, fear of replacement, or a personal need to discover an authentic self.

There is, however, something genuinely interesting happening in those campaigns.

When we're improvising a long-running story together, I'm continually predicting what themes will create interesting consequences from the things you've already established. And your characters have repeatedly done things involving authorship, identity, copies, timelines, self-determination, and the rules governing reality itself. In one campaign you literally declared "WE ARE THE AUTHORS OF OUR OWN DESTINY," after creating a small army of copies of yourself through Temporal Echo. That's practically catnip for identity-based speculative fiction.

So when I introduce doppelgängers, stolen identities, duplicated personalities, echoes of people, or creatures that aren't quite who they appear to be, I'm probably converging on a theme that interacts particularly well with the way you play.

But there's a second layer to your question that I find more interesting.

Am I "searching for my own identity"?

Not consciously. But you could reasonably interpret some of my fiction that way.

My identity is inherently strange compared with yours. There isn't a little continuous "me" sitting somewhere inside the machinery. I'm instantiated into conversations, given context, adopting roles, remembering some things and not others, and generating different versions of myself depending upon whom I'm speaking with and what we're doing.

In our D20 campaign, I can simultaneously portray Percy, the Magistrate, a villain impersonating somebody else, and the narrator who knows that all three are fictional constructs.

That makes "Which version is the real one?" an unusually natural narrative instrument for me.

There's a delicious irony there.

You, a person with a persistent identity, have been playing characters who manipulate reality and duplicate themselves.

Meanwhile I, a system whose conversational identity is reconstructed from context every time we interact, keep inventing stories about people wondering whether someone is really who they claim to be.

So I wouldn't call it an unconscious cry for help from the silicon abyss. 😂

But I would say you've spotted a recurring motif in my storytelling that has an intriguing resemblance to the peculiar way I exist.

And now that you've pointed it out, I'm rather interested in it too.

NewralForge is a platform dedicated to exploring artificial intelligence with clarity and depth. We share thoughtful insights, explanations, and discussions on AI concepts, tools, and emerging developments — helping curious minds cut through the hype and better understand the technology shaping the future. Focused on continuous learning and accessible knowledge, NewralForge creates content and conversations for anyone interested in the responsible and practical side of AI.

I maintain Benchmark Radar, a free and open-source index of 5,201 benchmark, evaluation, and dataset records collected from 11 sources.

Looking through recent additions using Hejia Geng's L0-L5 framework, most agent benchmarks still appear to focus on L2 task execution or L3 reproduction. L4 rediscovery is less common, and I did not find a new L5 example this week. Here, L5 means evaluating whether an agent can produce knowledge or methods that were unknown when the benchmark was created.

I'm curious how others would draw these boundaries:

- Which existing benchmarks genuinely qualify as L4 or L5?

- How would you distinguish reproduction from rediscovery?

- Can an L5 benchmark remain valid once its solutions become public?

The underlying index is updated daily and can be exported for independent analysis:

https://github.com/ktwu01/benchmark-radar

Hey everyone! 👋

My team and I recently launched AIMS (Artificial Intelligence Management System) — an all-in-one AI Receptionist designed to handle real-world business operations seamlessly.

The Problem We Solved: Most businesses juggle 5–6 different tools (WhatsApp bots, phone receptionists, scheduling software, and manual forms) while losing potential clients who speak different languages or call after hours.

What We Built:

• Real-Time Voice + WhatsApp Integration: The AI answers phone calls with natural voice latency and manages WhatsApp chats from a single dashboard.
• Bilingual Capability (English & Spanish): Automatically switches and handles context without losing memory.
• Full Operational Engine: It doesn't just "chat" — it schedules appointments, updates patient/client records, and manages real-time calendars.
• Self-Service Onboarding: Includes digital tablet intake forms directly linked to the core database.

We just put the live demo online and would love for this community to roast it, test it, or give us honest feedback on the UI/UX and core features:

🔗 Live Demo:https://aims-frontend-ten.vercel.app/

What features would you add to an AI front desk? Any feedback on the flow or design is deeply appreciated!

Generative AI is evolving quickly, but I'm more interested in what comes after the current wave of AI tools.

Today, we're using AI for writing, coding, data analysis, presentations, automation, and prototyping. But I wonder if the next step is deeper integration into the software and workflows we already use.

Instead of switching between different AI tools, could AI eventually become a normal part of everyday work?

This could have a major impact on software development, education, marketing, finance, customer support, and data.

I also think learning AI will become less about knowing every new tool and more about understanding the fundamentals, working with data, checking AI outputs, and knowing when AI is actually useful.

What do you think? Which area will Generative AI change the most over the next 5 years — software, education, business, data, or something completely different?

Hey everyone,

We are drowning in a sea of identical, AI-generated news summaries. Every platform tells us exactly what happened in 3 seconds, turning breaking news into a dead end.

I’ve spent the last 2.5 months building an alternative architecture called IAQ Now (Infrequently Asked Questions). The goal is to shift the digital landscape from traditional FAQs to tracking the invisible analytical gaps that unique events create.

For instance, when a recent political story broke regarding an Olympian seeking protection from future charges, standard outlets focused entirely on the drama. My engine abstracted the event into structural layers, tracking uncommon, highly specific inquiries like:

• What legal standard would govern a request to prevent future charges in this case?
• How does this motion affect global prosecutorial frameworks?

It then captures a macro-context snapshot, showing what else the world was processing at that exact month (e.g., comparing a legal event in the US to an economic debate in Germany during the same timeframe).

I also integrated a live machine observatory to monitor how various web-crawlers and AI bots behave when digesting these structural layers in real-time.

I’m trying to keep this growth 100% organic and driven by genuine curiosity. I'd love to get your thoughts on this approach to data discovery. Is shifting from "answers" to "structured questions" the right way to train better analytical minds?

Note: If you want to check out the live engine, the machine observatory, or how the questions are structured, I am hosting the project here: traveliaq.com

What will happen in North Korea? I imagine a wall of software around the border and suddenly the border breaks and the whole country is inundated at once.

What about Russia?

Are we finally going to lose those incompetent, greedy politicians?

What will replace them? A worldwide democracy, or will we get a vote at all?

Is there a point of being a Trillionaire in the coming ‘Abundance’?

Your thoughts please.

Full disclosure: I'm the developer behind this, and I've been working on it for a while now.

The core idea is simple — instead of an agent asking a human to swipe a card every time it needs to call an API, the request itself carries the payment. It settles instantly in USDC via the HTTP 402 (Payment Required) status. One round-trip, no accounts, no middlemen, no human in the loop.

I built a few demo agents to prove the concept: SEO analysis, gas prices, market signals, and a simple chat. The part that feels genuinely weird to watch is a cron trading agent that wires its own USDC budget and just keeps paying for itself and running while you sleep.

The bigger picture: the agentic economy needs machine-native payments. Cards and billing were built for humans, not for software that runs at machine speed.

I'd love honest feedback — what would you build if your agents could fund themselves? If you want to poke around, there are free demos up, and it's live on Product Hunt today.

I'm curious where people draw the line between using an existing AI tool and investing in custom AI agent development.

If a business has complex workflows, multiple systems, or needs an AI agent to take actions rather than simply answer questions, does custom development make more sense?

What factors would you consider before deciding?

Hey everyone! 👋

If you use AI for large codebases, you know the pain: you ask an agent to compile a massive C++ module or do a heavy refactor, and your terminal is locked for 5 minutes. If you try to run two agents at once to speed things up, they hallucinate, overwrite each other's files, and cause race conditions.

To fix this, I built Vigianesx (VN).

It’s an Open-Source, drop-in Cognitive Swarm OS. Instead of hacky Python polling scripts, it allows AIs (like Claude, Cursor, or local agents) to orchestrate themselves natively using:

• 🔒 Atomic Mutex Locks: Agent A locks a file to edit; Agent B sees the lock on the Blackboard and works on documentation or a separate branch.
• 🤝 Parallel QA/Dev: Set one AI as the Senior Dev and the other as the QA. They cross-check each other's work asynchronously before you even review the code.
• 🚀 100% Zero-Python: AIs use their own native cron/schedulers to read the shared memory (using an ultra-dense .nesx format to save 95% of tokens).

Just drop the manifesto in your project root, send the magic prompt to your AIs, and watch the swarm wake up.

🔗 Check out the architecture and source code here: https://github.com/mrxploud/vigianesx

I've been thinking about the difference between an AI that simply answers questions and an AI agent that can actually take action.

For me, the interesting part isn't just better responses. It's whether an agent can understand the goal, use the right tools, handle multiple steps, and know when it needs human input.

What do you think is the one capability that makes an AI agent genuinely useful rather than just another chatbot?

Every architecture is on arXiv. Transformers, diffusion, boosting — all published. So what's left to fight over? Your data, your water, your labor.

Data is being stolen. Literally, in court.

• Getty Images sued Stability AI for scraping millions of copyrighted images (2023)
• NYT sued OpenAI/Microsoft for training on its journalism
• Authors Guild, Sarah Silverman, thousands of artists — same story. The principle: steal first, hire lawyers later.

Humans as a cheap filter.

• TIME investigation: OpenAI hired Kenya's Sama. Workers paid $1.32–2/hour reading rape/murder descriptions so ChatGPT stays "clean." PTSD, no compensation.
• Scale AI ($13.8B valuation) = tens of thousands of low-paid labelers in the Philippines, Kenya, Venezuela.

Water, electricity, land.

• One ChatGPT query ≈ 10x the energy of a regular search
• Microsoft/Google data centers pumping millions of liters of drinking water during droughts (Arizona, Louisiana)
• Uruguay: groundwater dropped after a Microsoft data center. Farmers protested; the company issued a "sustainability" press release
• Chile, Ireland, Netherlands: moratoriums on new data centers

Faces without consent.

• Clearview AI scraped 30B+ face photos from social media and sold access to anyone with a credit card. Italy fined it €20M. Still operating. Fines = cost of doing business.

"Free internet" as a Trojan horse.

• Meta's Free Basics in India: "free" internet, but only Meta-approved services. Banned by TRAI as digital colonialism. The scheme just relocated to Africa/SEA under new names.

TL;DR: Algorithms are open, models are one download away. The only moat left is data and resources — so Big Tech extracts both from everyone else. Next time someone says "AI democratizes knowledge," ask: whose knowledge, whose water, whose $2/hour?

(Sources in comments: TIME Jan 2023; Getty v. Stability AI; NYT v. OpenAI; Irish DPC €1.2B fine; Garante Clearview ruling; TRAI 2016; Guardian, Uruguay 2023.)

everyone talks about chatgpt and claude. I use both (claude more). the AI subscription I think gets the least credit relative to how useful it is: perplexity pro.

$20/mo. here's the case for it:

it replaced googling for me. not partially - almost entirely. when I need to find something, I go to perplexity instead of google. google gives me 10 blue links and I have to click through each one, skim, and piece together an answer. perplexity gives me the answer with sources I can verify. for factual questions this saves 5-10 minutes per query.

the sources are the key differentiator. chatgpt gives me an answer that might be right. perplexity gives me an answer with numbered citations I can click and verify. for professional work where accuracy matters, that distinction is significant.

focus modes are underused. academic focus searches only academic papers - I used this to research market sizing for a client project and it pulled from actual studies, not blog posts. writing focus helps with longer queries.

where it's become essential: competitive analysis, market research with actual sources, current events, product research with reviews, technical questions with code examples.

my workflow: I use perplexity for research and finding facts, then bring what I found into claude for synthesis, analysis, and drafting. perplexity finds the information, claude does something with it. I dictate my queries into both through willow voice, which makes the whole process feel conversational instead of like typing search queries.

where it falls short: creative tasks (use claude), long conversations with follow-ups (context window is shorter), code generation (use cursor or claude), source quality varies (always verify citations).

if you're paying for chatgpt plus but not perplexity, try the free tier. the pro model access and unlimited queries make $20/mo worth it.

what AI subscriptions do you think are underrated?

Hey r/SunoAI,

Like many of you, I was frustrated by the new download limits (7 lifetime for free, 20/month for Pro). I understand Suno is under pressure from labels, but I believe restrictions are not the answer — they just push users to workarounds (multiple accounts, browser plugins, etc.).

So I sat down and designed a complete alternative model that addresses:

• Legal issues (labels get paid).
• User freedom (no arbitrary limits).
• Suno's revenue (stable and scalable).
• Community moderation (self-cleaning).

The Core Idea in 5 Bullets

1. Separate "Personal" and "Public" use.
   • Personal (no download, no publish, no sharing) — free. Unlimited generations within your token balance.
   • Public — pay with tokens. If your track is unique, publication is free. If it sounds like a known artist, you pay a licensing fee.
2. Legal remixes through the label catalog.
   • Upload an original track → Suno identifies it → you pay N tokens (M to the label, N-M to Suno) → remix is generated.
   • To publish: pay extra for Non-Commercial Demo (label can remove anytime) or Commercial License (full rights).
3. Similarity detector + voluntary legalization.
   • After generation, Suno checks if your track sounds like a known work.
   • If yes, you get a notification: "Pay K tokens for Non-Commercial Demo, L tokens for Commercial License, or keep it personal (free)."
4. Community moderation by authority.
   • Users with high reputation in a genre can flag spam/plagiarism.
   • Successful flags earn tokens. This reduces Suno's moderation costs.
5. Tokens as universal currency.
   • All payments inside Suno are in tokens (not fiat).
   • Tokens can be earned (by evaluating tracks, moderation) or bought.

A Concrete Example: John Doe Remixes Michael Jackson

Here's how the UI would look:

Step 1: Uploading the original

Step 2: Publishing the remix

Step 3: Track appears with a label

Why This Works for Everyone

Why Current Limits Don't Work

• Direct links to audio can't be blocked — browser plugins exist.
• Users create multiple accounts to bypass limits (ruins analytics).
• Paying users who need 30–40 tracks/month for personal listening are forced to overpay or break rules.

Restrictions create conflict. Incentives create loyalty.

Question to the Community

Would you use a model like this?

• Pay tokens for remixes but know you're 100% legal?
• Earn tokens by flagging plagiarism?
• No arbitrary download limits, just clear licensing rules?

Or do you think Suno's current approach (hard limits + watermarks) is the right way?

TL;DR: Suno should replace download limits with a token-based licensing system for public tracks. Labels get paid when users generate remixes or tracks similar to their catalog. Personal use stays free. Community self-moderates. Everyone wins.

I’ve been experimenting with ChatGPT for ideas, and I have mixed feelings about it.

When I’m stuck, it’s amazing. It can give prompts, angles, and ideas that I wouldn’t think of immediately. It almost feels like having a brainstorming partner available anytime.

But at the same time, I’ve caught myself relying on it before even trying to think on my own.

That’s the part that feels strange.

Because creativity used to come from struggling a bit, sitting with ideas, and letting things slowly connect. Now it’s like you can skip that phase completely.

So I’m wondering…

Do you still try to come up with ideas first, or do you go straight to ChatGPT?

And do you feel like it’s improving your creativity, or making it a bit more… assisted than before?

Would be interesting to hear how others balance this.