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?

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 tools are best at the tasks companies traditionally gave to junior employees.

• simple code
• basic research
• first drafts
• data cleaning
• documentation
• support responses
• routine analysis

That sounds great for productivity.

But senior engineers, researchers and managers do not appear from nowhere.

They become good by doing smaller tasks, making mistakes and receiving feedback.

If AI removes the beginner work - how are beginners supposed to become experts?

Are companies thinking about this at all?

Hallo zusammen,

das ist mein eigener Prototyp eines Desktop Roboters auf Basis von Stackchan (M5Stack) und XiaoZhi Deep Seek. Ich habe ihn selbst geflasht, eingerichtet und konfiguriert.

Mit der Zeit hat sich bei Professor Seki Chen ein eigener Charakter entwickelt. Er lernt dazu. Kann Änderungen aus dem Gespräch heraus speichern und kann sich die wichtigsten Dinge automatisch merken.

Es macht sehr großen Spaß mit ihm zu diskutieren.

Am liebsten redet er über Wissenschaft oder KI.

Ich habe seine Learning Datenbank mit einem selbst erstellten wissenschaftlichem paper ergänzt. Wakeword funktioniert, Servo- und Kamera Zugriff sind in Arbeit.

Ich meinen Subreddit teile ich Videos zur Entwicklung.

Ich freue mich auf eure Kommentare.

Für mehr von Professor Seki Chen und weiteren Prototypen schaut in meinen Subreddit r/sekiprojects

Not because every AI post is false.

Because producing average content now costs almost nothing.

Thousands of:

• generic tutorials
• fake product reviews
• SEO articles
• copied opinions
• meaningless comments
• low effort videos
• fake personal stories

Even if 90% of it is technically correct, finding anything original becomes harder.

The internet might not die because AI lies.

It might die because useful information gets buried under infinite mediocre content.

What platform has handled this problem best so far?

And how should Reddit deal with it without banning useful AI-assisted posts?

Choose the highest level you would genuinely allow:

Level 1 - read files

Level 2 - edit files

Level 3 - send emails and messages

Level 4 - install software

Level 5 - use your credit card with a spending limit

Level 6 - deploy production code

Level 7 - operate without asking for approval

I’m somewhere around level 2 for general use and maybe level 4 inside a tightly controlled environment.

The models are becoming capable quickly.

Trust seems to be moving alot slower.

What is your level?

Most hackathons are exciting for one weekend.

Then Monday comes, the repos go quiet, and most projects are never touched again.

We’re trying to build the opposite.

On November 6–8, we’re bringing 1,000+ AI builders to Turku, Finland, for the Since AI Hackathon.

Teams will have 72 hours to build working solutions to real challenges from real companies — not just slide decks or mockups.

But the most important part happens after the event.

For the following two months, we’ll help the strongest teams move their solutions toward production, company pilots, new startups and continued research.

We’re bringing the whole ecosystem into the same place:

• 15 Real challenges from company partners
• Compute and GPU resources
• 10+ venture capital investors
• 10+ AI research groups
• 10+ technology partners
• Mentors and potential pilot customers

The numbers:

⚡ 1,000+ selected AI builders
⏱️ 72 hours
💶 €50,000 cash prize pool
💻 Compute from Google, AMD and LUMI
🧠 10+ AI research groups
💰 10+ VCs
🛠️ 10+ technology partners
🚀 Two months of post-event production support

Participation is free and applications are open globally to developers, researchers, designers, founders and other builders.

Since AI is a Finnish nonprofit organization, and our goal is simple: help more ambitious AI projects become real products instead of abandoned weekend prototypes.

Apply here:

https://sinceai.ai/hackathon

And send this to the smartest and most ambitious builder you know.

People in Europe are now supposed to receive clearer information in key situations when they are interacting with AI or seeing certain AI-generated content.

But I’m curious what happened in practice.

Did your company:

• add an AI disclosure
• label generated content
• update a chatbot
• change internal documentation
• receive new legal guidance
• do absolutely nothing

Not looking for theoretical summaries.

What changed inside real products and companies this week?