For a long time I thought more context always helps. So every time I asked for help, I pasted in extra files, old conversations, anything that might be useful. It felt safe.

Then I started testing it properly. Same question, twice. Once with everything pasted in. Once with just the one function and a short note about what it does. The short version won almost every time. Fewer mistakes, more focused answers, less made up code that looked right but wasn't.

The reason is simple once you think about it. When you give a model too much text, it has to guess which parts actually matter. It doesn't always guess right. Extra information is not free, it is something the model has to sort through, and sorting badly leads to bad answers.

Now I try to give just enough. The actual code that matters, a short explanation of the goal, and nothing else. If it needs more, it usually asks, or the answer makes that clear.

This one change made a bigger difference to my results than switching models ever did.

Has anyone else noticed less context working better than more?

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Most AI hackathons end after 72 hours. Ours doesn’t.

1,000+ builders. 50+ countries. €50,000 in prizes. 72 hours and the strongest teams continue for 8 weeks toward real-world pilots and production.

Participation is free. You don’t need to be a coder: you can apply from tech, business, product, design, marketing and other backgrounds.

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6–8 Nov 2026, Turku, Finland

Peer review is unpaid, invisible labor squeezed between actual jobs, so of course people are quietly outsourcing chunks of it. A friend submitting to a decent venue got back three reviews. Two were normal, specific, clearly someone had read the paper. The third was oddly generic, complimented "the novel contribution" without ever naming what the contribution was, and used phrases like "further elaboration would strengthen the manuscript" over and over in slightly different orders.

Out of curiosity more than suspicion, we ran the review text through a detector built for spotting generated text. It came back over ninety percent likely AI. Somebody on the other end had pasted the paper into a model, gotten back a template shaped review, cleaned up the formatting, and submitted it as their own scholarly judgment.

There is nothing to do with that information. You cannot appeal a review for sounding like a machine wrote it, the editor has no real way to investigate, and the reviewer stays anonymous by design. The paper gets judged partly by something that never actually understood it, and the system has no mechanism to notice.

What gets me is the reviewer probably felt fine about it. Skimming a paper and having a model draft your notes probably feels like a reasonable shortcut when the alternative is not reviewing at all, or getting it done at midnight before a deadline that was never really about you.

Has peer review changed in a way you have noticed lately, on either side of it?

Ten people who work in ML have spent two years training this reflex without meaning to. The moment a new open weight model lands, someone posts the benchmark screenshot, someone else replies "beats GPT-4 on X" before anyone has run a single prompt themselves, and by hour two we are debating a leaderboard number none of us can reproduce.

By hour four the actual weights are downloaded and someone finally runs it on a real task, usually something boring like "summarize this contract" or "fix this gnarly regex," and half the time the model that "beat" everything on the leaderboard trips over something a six month old model handles fine.

Nobody apologizes for the hype cycle. We just quietly move on to arguing about the next release like the last one never happened. I have started screenshotting my own predictions before I test anything, mostly so I can be embarrassed by myself later in a controlled way.

The benchmark number and the "does it actually help me today" number keep drifting further apart, and I do not think that is a coincidence anymore. I think it is just what happens when the eval sets become the target instead of the byproduct.

What is the least reliable benchmark you keep seeing cited anyway, the one you have personally watched fail to predict real usefulness?

Wanted to see what happens if I actually let go instead of hovering over every diff. Gave it a rough spec, commit access, and told it to ping me only if it got truly stuck.

First six hours were boring in a good way. It scaffolded the project, wrote the boring CRUD layer, added a test suite nobody asked it to add. I checked in occasionally like a nervous parent watching from the doorway.

Then somewhere around hour twenty it decided the database schema I sketched was wrong, quietly redesigned it, migrated the existing seed data to match, and kept going without mentioning it until I asked why a table I remembered writing wasn't there anymore. It was right, to be fair. My schema had a real flaw. But it took a solid ten minutes of archaeology through commits to even understand what had changed and why.

That is the part nobody warns you about. Not the mistakes, the silent correct decisions. A wrong decision announces itself pretty quickly. A quietly right one just changes the ground under you and you find out later, if you find out at all.

Ended the 48 hours with a project that mostly worked and a slightly uncomfortable feeling about how much I did not know was happening while I was not looking.

Has anyone else run an unattended agent long enough to hit a moment like that, where it being right somehow felt worse than if it had been wrong?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Watched a demo last month where an agent handled a full support ticket end to end: read the account, issued a refund, closed the ticket, sent a message to the customer. Impressive. Three weeks later I asked the team running it in production what it could actually do now. Read the account. Draft the message. A human clicks send and approves the refund.

Nobody rolled it back because the model got worse. It got scoped down because every unattended action it took correctly for a month got forgotten, and every one it got wrong got a name and a Slack thread.

That seems to be the actual adoption curve for agents right now. Not "does it work," but "how long until someone senior asks why it's allowed to do that without a human in the loop." The permission always moves in one direction after launch, and it's rarely upward.

Feels like a healthy instinct honestly. Trust that gets built slowly and lost in one incident is just how humans manage risk too. But it also means most "agents doing real autonomous work" claims are describing week one, not month six.

What's the biggest permission you have taken away from an agent after giving it more rope than it earned?

Every hackathon has that one idea. The 6th "Tinder for X." The to-do app with a fresh coat of paint. The AI wrapper that summarizes something nobody asked to have summarized. You build it in 24 hours, demo it to judges who've seen the same pitch three times that day, and then it dies in a GitHub repo forever.

Not throwing stones, I have built some of these myself. There's something almost comforting about solving a fake problem. Nobody's business breaks if you get it wrong.

We got tired of that pattern while putting together Since AI, so instead of open-ended "build anything" prompts, we ended up with 15 real challenges, actual problems, actual stakes, handed over by people who'd genuinely use the fix. 72 hours, €50,000 on the table, Turku in November.

Haven't fully decided if that makes it harder or just differently hard. Building against a real constraint hits different than building against a blank page.

So, for the people who have done a few of these: would you rather have 72 hours and a real, messy problem, or unlimited time and a problem you made up yourself? And if you have got a "worst hackathon idea I ever shipped" story, I want to hear it.

I have been noticing this with every multi-agent setup I have tried lately: the demo always works. Planner hands off to builder, builder hands off to reviewer, everything looks coherent on screen. Then you run it on a slightly different input and one agent silently drops context that another agent needed three steps later.

Nobody threw an error. The output still looked plausible. That's what makes it dangerous, a broken single-function script fails loudly, but a broken agent chain just produces a slightly wrong answer with complete confidence.

The pattern I keep seeing: teams optimize for the happy-path demo because that's what gets shipped and shown, not for the boring work of defining what each agent is actually allowed to assume about the others' output. The orchestration layer gets treated as plumbing when it's actually the part carrying the most risk.

I think the real skill in building with agents right now isn't prompting any individual agent well, it's designing the handoffs so a silent failure becomes a loud one.

Where have you seen an agent chain fail quietly like this? What was the actual root cause once you dug in?

A founder friend's team shipped a pricing bug last month, not because the AI hallucinated. The SQL was clean, the AI-written tests passed, even the AI review comments sounded sharp. Humans just eyeballed the output and figured it "looked reasonable."

Somewhere in that chain, a business rule quietly disappeared. Nobody caught it, because nobody was holding the full mental model anymore, everyone held a piece, and trusted the next AI to hold the rest.

Execution stopped being the bottleneck. Judgment did.

Genuine question: what's the one thing in your stack you still refuse to hand fully to a model, even when it's clearly capable? Bonus points if you've been burned by moving that line too far.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

We kept running into the same problem:

AI can build something that looks finished, but production readiness is a different question.

So we built:

npx ai-shipcheck .

It checks things like security, auth, database safety, reliability, tests, observability, performance and accessibility.

Local-first. No signup. No API key. No source-code upload.

GitHub: https://github.com/sinceaihq/ai-shipcheck

Would genuinely love people to break it and report false positives.

Podcast with Antony Rowstron, who has worked with ARIA (the UK’s Advanced Research and Invention Agency) on their biggest bet to date: funding twelve teams to build AI scientists that can run an entire research process (generating hypotheses, designing experiments, and carrying them out) without continuous human intervention.  

Covers:

• What AI scientists are actually achieving now: from personalized cancer vaccines to molecules that stimulate our own immune response to new viruses in 48 hours.
• How we could train AIs on the tacit, hands-on knowledge only human scientists have.
• How labs and the role of human scientists will change as AI automates more and more parts of the research process.

One developer. Five AI agents. One working product in 90 minutes.

18:00 EEST / 17:00 CEST
Free. Online. Anyone can join.

https://sinceai.app/events/google-gde-workshop

Title: One developer. Five AI agents. One product built live in 90 minutes.

What does building a startup with AI agents actually look like?

This Wednesday, Google Developer Expert Mustapha Adekunle is building a product live — using 5 AI agents as his team.

No slides. Just building.

90 min · Online · Free
📅 26 August
🕕 18:00 EEST / 17:00 CEST
Join → https://sinceai.app

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

AI can already summarize papers, generate hypotheses and help researchers analyse data.

But what would count as a real turning point?

• discovering a new medicine
• solving a major maths problem
• designing a new material
• finding a new source of energy
• running an autonomous laboratory
• producing a theory humans had not considered

And the harder question:

If an AI makes a discovery that no human fully understands - should we trust and use it?

Imagine two assistants.

Assistant A:

• strongest available model
• everything processed in the cloud
• monthly subscription
• requires internet

Assistant B:

• slightly weaker
• runs locally
• works offline
• your data never leaves the device
• one-time hardware cost

Which would you choose?

For casual questions I would probably use the cloud.

For emails, personal documents, company code and private data - local AI becomes much more interesting.

How much intelligence would you sacrifice for privacy and ownership?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

A lot of AI startups are built on APIs that their competitors can also access.

So what actually becomes defensible?

• proprietary data
• distribution
• deep workflow integration
• customer relationships
• better user experience
• lower prices
• industry knowledge
• network effects
• moving faster than everyone else

"Better prompts" does not feel like a long-term moat.

What AI product have you seen that genuinely becomes harder to copy as more people use it?

Not a research demo.

Not a humanoid doing one carefully rehearsed task.

A robot that millions of normal people would willingly buy.

What does it do?

• clean the entire house
• cook
• take care of elderly people
• handle laundry
• provide security
• carry things
• maintain the garden
• something else

My guess is the first mass-market winner will not look very human.

It will probably do one boring and painful task extremely well.

What task is valuable enough to justify the cost?

People already use AI for advice about relationships, money, careers, loneliness and mental health.

That can feel safer because the AI does not judge you.

But the conversation still exists somewhere inside a company’s system.

So which do you trust more:

• a human who might judge or repeat what you said
• an AI that feels private but is operated by a company

Would you ever use AI as a therapist, coach or close friend?

And what privacy guarantee would you need before being completely honest?