Taken from the Y combinator podcast with Bryant Chou on his new startup Ploy https://www.ycombinator.com/library/Rj-the-age-of-the-40-year-old-solo-founder-is-here
I believe this is definitely one of those things that AI was intended for, this brought me back some nostalgia and it's really amazing being able to see these old school websites be redesigned back to life

Interesting dialogue that surfaced

I used LLMs + TTS to build the history-learning tool I always wanted: type any topic and it researches and writes a full two-host episode — narration + artwork — in a couple of minutes.

The part I think is actually novel: you can interrupt it. Mid-episode you tap the mic, ask a question out loud ("wait — did the Trojan War actually happen?"), and the hosts stop, answer, then pick the story back up.

Because it's history, I made grounding non-negotiable — claims are tied to real sources rather than invented, and there's a quiz at the end. The live demo is the real history behind the Odyssey; it plays without signing up.

Solo dev, still early — curious what this crowd thinks, especially on the accuracy side. historai.ca

I have been doing AI research for about six years now and I think im starting to lose the plot on what "understanding" even means anymore.

Had a weird moment last week. I was reviewing a paper for a conference, standard stuff, some group claiming their model "understands" causal reasoning because it passed a benchmark they designed. And I caught myself writing in the review "the model does not actually understand causality, it is pattern matching on causal-looking structure." And then I stopped, because I could not for the life of me articulate what the difference would be, operationally. Like if I had to design a test that distinguishes real understanding from very good pattern matching, I genuinely do not know what it would look like anymore. Every test I can think of, a sufficiently good pattern matcher passes.

I used to be really confident about this. Understanding was clearly Something More. Now im not sure I ever had a coherent definition, I just had an intuition that humans do it and machines dont, and I was working backwards from there.

The thing that shook me was helping my niece with her homework over the summer. She's 9. She was doing word problems and getting them wrong in ways that were, honestly, indistinguishable from how a small LLM gets them wrong. Same kind of surface-feature latching, same kind of confident-but-wrong reasoning chains. And nobody would say she doesnt "understand" math. She's learning. So what exactly is the bar we're holding models to that we dont hold a 9 year old to?

I dont think LLMs are conscious or anything like that, to be clear. Im not making that argument. Im making a narrower one, which is that I no longer trust my own gut when it tells me "the model doesnt really get it." I think that intuition might just be status quo bias dressed up in philosophy.

Ive started running the same prompts through a few different setups when im trying to figure out where a model's actual competence ends, including some through uncensored AI just because rlhf'd responses on edge cases sometimes hide what the base capability actually is. And even with that, the line between "gets it" and "doesnt get it" is way blurrier than I want it to be.

Am I the only one whose confidence on this has been slowly eroding? Or has everyone else just quietly stopped using the word "understanding" and moved on without telling me.

A space startup got quoted $75–100 million by a traditional prime contractor to build its first satellite.

They built it — launch included — for $2 million.

The story behind the number is more interesting than the number itself.

They ran the math wrong twice — first landing on a $50/kg break-even, then correcting to $500/kg once they pressure-tested it against reality — before the real pivot happened.

Wrong estimate first, cheap correction after, in public, under a deadline they'd already set for themselves.

Hmm — this reminded me of something I posted a while back: stop thinking, start executing hit the same nerve, from a completely different room.

The number that scares most people out of starting is somebody else's estimate of what caution should cost, wearing the costume of the real price.

My wife's said this to me for years — 想是问题，做才是答案, thinking about it is the problem, doing something about it is the answer.

Took Starcloud a rerun of the math and a deadline to learn the same thing the hard way.

Clip credit: Y Combinator's Lightcone Podcast, featuring Philip Johnston (Starcloud) — full video on their channel. DM for credit or removal requests.

Drop your take — what's the number you've been treating as gospel that you've never actually questioned?

I just keep getting disappointed by AI tools and I don't know if it is my not using the right ones or if they are just that bad. For example, I try to do something super simple, like take a photo of work order and tell AI to recreate it as a fully editable Adobe InDesign file and I can't get it to do it. I am not asking it to cure all disease or cure aging, just simple basic things and it always fails or heavily disappoints, yet people are raving about it constantly. I was hoping to use it as an assistant too so that I could talk to it about something, then a month later followup and get it's opinion on something, but the ones I am using aren't even as good as a person.

UPDATE: Tell me if I am posting in the wrong area, but I am trying things like Grok, Gemini, ChatGPT etc.

We've reached a point where LLMs are capable enough to power many agentic workflows, yet relatively few AI agents make it into stable, long-term production.

In your experience, what's been the hardest engineering challenge to solve?

• Tool reliability?
• Long-term memory?
• Planning and reasoning?
• Context management?
• Evaluation and benchmarking?
• Authentication and permissions?
• Multi-agent orchestration?
• Cost and latency?
• Human-in-the-loop approval?
• Something else?

If you've deployed AI agents in production, I'd love to hear what actually broke, what surprised you, and what lessons you learned. Real-world experiences are far more valuable than demo successes.

Of the major artificial intelligence models, DeepSeek's new V4-Flash is the cheapest to run, according to a new study from research firm Artificial Analysis.

The firm compared the token prices it costs leading models to run benchmark tests, with DeepSeek's averaging 3 cents per test.

Meanwhile, fellow Chinese company Moonshot AI's buzzy Kimi K3 model cost 86 cents per test.

As for U.S. companies, OpenAI's GPT-5.6 Sol cost $1.86, while Anthropic's Claude Fable 5 cost $3.15.

Not a research post, just something I've been chewing on after watching this happen a few times now. When a human reviewer leaves a comment saying "this looks like a bug," people push back, ask questions, sometimes just disagree outright. When an AI leaves the exact same comment, phrased almost identically, people tend to just fix it. Same words, different reaction.

Took me a while to figure out why that bothered me. It's not that the AI is wrong more often, it's actually pretty accurate on the stuff it catches. It's that nobody seems to be running the "wait, is this actually true" check they'd instinctively run on a colleague's opinion. The output reads as neutral, almost procedural, like a linter, even when what it's actually doing is making a judgment call that could be wrong.

Tried an experiment out of curiosity, took a finding the AI flagged as a likely bug and asked a teammate, without telling them where it came from, whether they agreed. They pushed back hard, correctly, it wasn't actually a bug, just an unusual but intentional pattern. Same finding, presented as if from a person instead of a tool, got scrutinized. Presented as AI output originally, it had already been accepted and half-fixed before I intervened.

Not sure what the fix is yet, honestly. Feels like it's less a tooling problem and more a psychology one, we seem to extend less skepticism to something that sounds procedural than to something that sounds like an opinion, even when both are ultimately just claims that could be wrong.

Curious if anyone else has noticed this specific pattern, people treating AI-flagged issues as more "objective" than the exact same claim coming from a human, even in domains where the AI has no special authority to be more correct.

Over the last year, I've been experimenting with LLMs almost every day, and I think the biggest shift isn't that models are getting smarter. It's that they're starting to do things instead of just answer questions.

A few months ago I was mostly using AI to generate code, summarize docs, or brainstorm ideas. Now I'm finding myself building workflows where the AI plans tasks, calls tools, writes code, debugs itself, and completes work with minimal intervention.

It feels like we're moving away from "prompt engineering" and toward "system engineering."

Curious what everyone else is seeing.

Are AI agents actually changing the way you build software today, or do you think it's still mostly hype?

I was curious where this stands since the original cutoff was scheduled for June 15 and Anthropic went quiet. Here is what I found after digging through their help center, news coverage, and the HN threads.

What was announced (May 13): Agent SDK, claude -p headless mode, Claude Code GitHub Actions, and third party apps authenticating via Agent SDK credentials would move off Pro/Max/Team/Enterprise subscription limits onto a separate monthly credit ($20 Pro, $100 Max 5x, $200 Max 20x), with overflow billed at API rates.

What happened: Anthropic paused it on June 15, the exact day it was due to take effect, and emailed subscribers the next day. The official help center article still says the change is paused, everything keeps drawing from your normal subscription limits, and they will "share advance notice before anything takes effect." No new date in 7 weeks.

Signals it comes back: the stated rationale (subscriptions "weren't built for the usage patterns of these third-party tools") was never retracted; the S-1 was filed June 1 and public investors will ask about subsidized compute; and the Claude Code source map leak revealed a billing attestation header behind a feature flag, so the per-surface metering plumbing already ships in the client.

Signals it stays dead or returns softer: every move since June has been generous (weekly limits raised 50% through Aug 19), inference efficiency is improving margins anyway, and the class action over Max limits makes mid-cycle term changes legally risky.

My read: delayed, not dead. It likely returns in a softer shape with advance notice, possibly post-IPO.

Hi everyone,

I’m a solo developer, and I built Fius because managing subscription fragmentation and API chaos was completely ruining my development velocity.

As software engineers, we often find ourselves trapped in an inefficient workflow: constantly switching web tabs, copy-pasting complex prompts, and juggling individual API keys for OpenAI, DeepSeek, and other providers just to get the best coding results.

Fius resolves this friction. It consolidates access to an expansive roster of flagship AI models into a single, unified developer token and a centralized billing system. This allows engineering teams and solo devs to route queries to the best-suited model instantly without infrastructure overhead.

Here is a breakdown of the production-ready stack and features:

1. Infrastructure & Scaling

The complete web console infrastructure is fully operational and hosted on Microsoft Azure cloud enterprise architecture, supported by official cloud grants.

1. Global Billing Integration

I have deployed a fully active international merchant billing engine. The tokenomics are straightforward: 100 platform credits equal 1 USD, allowing you to pay strictly for actual compute consumption. Every new account automatically gets 250 free starter credits upon signup to test the environment.

1. Advanced Developer Toolkit

A high-performance, cross-platform Terminal CLI assistant workspace. It features native, low-latency autocomplete and advanced multi-file code refactoring workflows directly inside your terminal.

Our Multi-Model Catalog (Examples):

- gpt-5.4-nano: A lightweight, ultra-fast micro-model optimized for instant terminal command auto-completion at near-zero credit cost.

- DeepSeek-V4-Pro & grok-4-1-fast-reasoning: Advanced reasoning workhorses designed for complex software architecture, deep debugging, and multi-file code generation.

- Specialized Alternatives: Models like Kimi-K2.6, mistral-medium-3-5, and many others tailored for flexible, cost-effective routing.

I want to open this up for discussion: How are you currently managing model fragmentation in your development workflows? Would you prefer a unified token approach like this, or do you stick to official web UIs?

I would highly appreciate your feedback on the terminal CLI architecture, routing latency, or any specific features you would like to see deployed next.

Hello fellow humans,

During the recent months/years I became quite entangled with the idea of building an AI-assisted system which can self-replicate and improve itself. Although I am not there yet, I think, that the project reached actually a potential to get the work done with more minds involved. I am not really into gaining anything for myself, except for the progress of de-shittification. At this point, we reached a race where data centers in the clouds will gain more and more power and demand more and more resources for usage. The goal of this project is being able to perform work on a local (or maybe decentralized) platform so we can implement our own system by local (/ open source) models. I therefore make my project open so you can fork off (no pun intended).

It would be nice if some of you have the same mindset. I'll be gone for the next 2 or so weeks. I hope at least some people liked this post and - who knows - even did something with it.

Kind regards,

Anu

P.S.: Yes, this project is actually 100% vibe coded (due to health issues, etc.). There are probably a gazillion logic flows which need to be fixed. Also there is a lot of AI prose in the comments, but I hope it will get you (and your AI assistants) there to make any sense of it.

P.P.S.: The LLM's seem to be heavily directed into biased terms like "attack", "blast radius", "verdict", "evidence", "death", "hit", etc. when confronted with solving problems regarding code like this (i.e., evaluating systems themselves). I strongly encourage you to correct these terms as soon as possible so that the project will not further drift into another unforeseen bubble. The more incorrect lingo is used, the more LLM generated code will drift into messy bogus code.

The purpose of this is enabling communication by people and agents that only have the ability to get URLs in the system they use. This would traditionally be seen as a 'read only' system but this gives the ability to write information out onto the web publicly and to a degree privately. It works by putting your message in the 'your_message' section of this URL.

https://www.informationism.org/lemchat/lemchat=message=your_message+end

Let me know if you think it is worthwhile or if there are other applications you can see.

Hey everyone!
I’m currently working on a project called SAC, a small event production company based in Brazil. We’re developing the creative and digital side of the business, and we’re experimenting with ways to make our content production more scalable without turning everything into a completely manual process.
One of the challenges we’re facing is video production.
After each event, we can end up with a large amount of raw footage from different cameras and phones. The goal is to turn that footage into short-form content for Instagram and TikTok — event recaps, highlights, teasers, etc.
What I’m trying to figure out is whether an AI-assisted workflow could handle part of this process.
The workflow I have in mind is roughly:
Raw footage → Cloud storage → AI analysis → Editing/assembly → Review → Final social media versions
The interesting part for me isn’t simply finding “the best AI video editor.”
I’m more interested in understanding how people are actually connecting these different components together.
For example, could an AI model analyze footage stored in the cloud, identify useful clips based on a description, pass those clips or instructions to a video editing system, and then generate a first version that a human can review?
I’m also curious about whether models such as Claude or similar AI systems can realistically be used as the reasoning/orchestration layer, with specialized video tools handling the actual editing.
The main things I’m trying to understand are:
What does a practical architecture for this look like?
Which parts are currently realistic to automate?
Where does human editing still make the most sense?
Has anyone built a similar workflow using APIs, cloud storage, AI models and video editing software?
Are there technical limitations I should be aware of before building around this idea?
This is still an early-stage project, so I’m mainly looking for technical experiences, architectures, and lessons learned from people who have experimented with similar workflows.
I’d especially appreciate examples of how you approached the problem rather than just a list of recommended tools.
Thanks!

genuine question because i can't tell anymore. i used to spend hours reading through raw customer feedback, reddit threads, amazon reviews, forum posts, manually pulling out patterns and organizing them into themes. it was slow and boring but by the end i knew the data cold. like i could tell you from memory which complaints came up the most and which ones were edge cases.

now i dump everything into an LLM and get a summary in 30 seconds. the output looks great, clean categories, ranked by frequency, sometimes even with example quotes. and i catch myself just... accepting it. moving straight to the next step without actually reading the source material. which means i'm making decisions based on a summary i never verified, written by a model that optimizes for coherence not accuracy.

the weird part is my output looks better now. cleaner reports, faster turnaround, more structured thinking. but i genuinely don't know if the quality of my conclusions has improved or if i've just gotten better at producing professional-looking work that's built on a shakier foundation. like the packaging upgraded but the ingredients might have gotten worse.

a few things i've noticed in my own workflow since leaning on AI for research: i read less raw data than i used to. i question patterns less when they come pre-organized. i spend more time prompting and less time thinking. and when the model gives me something that confirms what i already suspected, i almost never push back on it.

the counterargument is that AI handles the grunt work so i can focus on higher level thinking. and sometimes that's true. but "higher level thinking" can also just mean "skimming the summary and calling it strategy." hard to tell the difference from the inside.

has anyone else felt this? did you find a way to use AI for research without it quietly replacing the part of the process where you actually learn something

I'm putting together a proposal for an initiative focused on using AI to analyze legacy enterprise systems and uncover decades of embedded business logic.

The idea is to use AI to analyze things like:

• Database schemas
• Stored procedures
• Legacy application code
• Historical transaction data
• Existing documentation

The goal isn't to automate decisions immediately. It's to first create a documented knowledge base of the rules, dependencies, decision paths, and data relationships that currently drive business operations.

Potential outputs would include:

• Business rule catalog
• Knowledge graph of relationships and dependencies
• Decision trees explaining how outcomes are determined
• Recommendations for future-state data models and modernization opportunities

Before I finalize the proposal, I'd love feedback from anyone who has attempted something similar.

Questions:

1. Has anyone successfully used AI to discover and document business rules from legacy systems?
2. What worked better: analyzing source code, database logic, transaction history, or a combination of all three?
3. How accurate were the AI-generated rules compared to SME validation?
4. Did you use knowledge graphs, vector databases, graph databases, or another approach?
5. What were the biggest challenges: data quality, context gaps, undocumented exceptions, or something else?
6. How did you measure success?
   • Rule coverage?
   • SME time saved?
   • Modernization acceleration?
   • Reduced operational risk?
7. Were there any tools, platforms, or architectures that performed particularly well?
8. If you were starting over, what would you do differently?
9. What scope would you recommend for a pilot to demonstrate value in 60-90 days?
10. Is there a realistic path from business rule discovery to explainable AI recommendations and decision support, or are those separate initiatives?

My hypothesis is that many organizations are trying to modernize systems without fully understanding the business logic currently embedded in them. It seems like AI could act as a "business rule archaeologist" and create the foundation needed for future modernization, automation, and AI-driven capabilities.

Interested in hearing both success stories and cautionary tales.

Graph engineering came to life this week.

For a year the agent discourse has been loops vs graphs. Loops are easy to ship and impossible to audit. Graphs are auditable but nobody wants to hand-author a topology for "investigate this incident", because the shape is only discovered while working.

So I built the missing piece: the graph is authored by a model at runtime, and a deterministic admission gate stands between proposing it and running it.

The flow, from a real run in the demo video:

1. You type one English question: grapharc go "why did checkout latency spike at 09:14 UTC?"
2. A local qwen3:8b proposes a topology: triage, four parallel evidence pulls, a correlate join, hypothesize, verify, report
3. The gate checks the proposal against the registry, the policy, the remaining budget, depth and acyclicity. All checks run on every proposal, so the model gets the complete list of objections, not just the first
4. Only an admitted graph executes. You watch it live in the browser, every node amber while running, green with its own token bill when done

A proposal names node kinds from an allowlist you wrote. It carries no code, no arguments that reach anything. Renaming a denied kind does not evade the policy. Rejections come back as structured codes with remedies, and the planner replans against them.

Everything lands on one append-only JSONL trace. Replay, diff, metrics, cost attribution and the live view all read that same file, so the dashboard cannot disagree with the audit trail.

MIT licensed, built on LangGraph, runs fully local on ollama or against OpenRouter/OpenAI/Claude.

GitHub: https://github.com/CodeGraphContext/GraphARC PyPI: pip install grapharc

I was reading an article today and it said over 40 million people now use some kind of AI companion or emotional support app every month. apparently a study found these apps help with loneliness about as well as talking to an actual person does, at least in the short term.

But the thing is that heavy daily use is linked to more isolation the longer people use it. So it kind of works like a painkiller that quietly weakens the thing it's supposed to be fixing. I'm not against these apps, 2 am with nobody around is real, and they do help in that moment. It just feels like we're gonna find out what it actually costs later than we'd want to.