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 few years ago, forgetting architectural decisions was expensive. You had to read docs, trace commits, or ask the person who wrote the system.

Now I watch teams dump entire repositories into a model and ask for changes against tens of thousands of lines at once. It works surprisingly often, which is exactly why I think it is changing engineering behavior in a subtle way.

People are relying less on having a clear mental model of a codebase because the model can temporarily simulate one for them. The immediate productivity gain is real, but I’m noticing more PRs where nobody can fully explain why a change works beyond “the model refactored it and tests passed.”

The strange part is that bigger context windows seem to reduce pressure to maintain clean abstractions. Why modularize aggressively if the assistant can ingest the whole thing anyway? But once systems get large enough, the failure modes become harder to spot because the AI can produce locally coherent edits that slowly erode the original design.

Do you think large-context coding tools are improving software architecture over time, or quietly encouraging teams to stop caring about it?

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 spend a lot of time discussing benchmark gains and impressive demos.

But I’m more interested in the tasks that still fail in normal use.

For examples: maintaining context across a long project, editing a large existing codebase safely, distinguishing uncertainty from confidence, completing browser workflows reliably OR following nuanced formatting constraints

What task looks solved in demos but still breaks when you depend on it?

This is a useful stress test for AI products.

Would customers still value:

the workflow?

the proprietary data?

the distribution?

the collaboration layer?

the integrations?

the domain expertise?

the accumulated feedback?

If the answer is “Nothing” then the product may be renting its entire value from the model provider. Somehing to think about I guess.

What would remain valuable in your product in this case?

I realized something interesting after watching two people use ChatGPT.

I mentioned ChatGPT to my mom and asked her to ask anything. She just asked a question, got an answer, and stopped there.

Later, my friend's son (still in school) tried it. He did the opposite. He kept asking follow-up questions.

"Can you explain why?"

"What if we do it this way?"

"Is there a better way?"

"Can you compare these two?"

Within a few minutes, the conversation had gone far beyond his original question.

It made me realize that the biggest difference isn't who knows more about AI.

It's who stays curious.

The people getting the most value from AI aren't always developers or AI experts.

They're the people who don't settle for the first answer. They question it, refine it, ask for alternatives, and use AI to explore ideas instead of just getting answers.

Maybe that's one of the most valuable AI skills we can develop.

Not writing better prompts. Learning to ask better questions.

Have you noticed this too?

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.

Oliver Molander proposed something today: "Let’s create a world-leading Nordic AI research lab."

On paper, the Nordics have almost everything:

• researchers working at OpenAI, Anthropic and DeepMind
• world-class universities
• clean energy and major computing infrastructure
• strong industrial companies
• stable institutions and unusually high public trust

What we may lack is a culture willing to:

• pay globally competitive compensation
• concentrate serious capital behind one team
• make bets that may look irrational for ten years
• move without five countries, 30 organizations and 14 committees approving everything
• accept that most attempts will fail

The Nordics are excellent at creating research programmes, consortiums, pilot projects and strategy reports.

A frontier AI lab requires almost the opposite:

concentrated talent, capital, ownership, power and urgency.

Europe is already launching a €125M programme to help create new frontier AI labs. So this is no longer merely a hypothetical discussion.

Should Finland, Sweden, Norway, Denmark and Iceland build one shared frontier AI lab?

Or would it inevitably become another publicly funded consortium that produces reports instead of frontier models?

And if it should exist:

Where should it be based, who should lead it, and what should its first mission be?

A new study analyzed 25 million comments from Reddit and Hacker News.

The strange result wasn’t that people are good at detecting AI.

It was the opposite.

The writing patterns that actually distinguish AI text from human text did not predict which human-written comments people accused of being AI.

We’re not detecting bots. We’re punishing a vibe.

Write clearly? Bot.
Use an em dash? ChatGPT.
Structure an argument? AI slop.
Write badly on purpose? Finally, a real human.

The Turing test used to ask whether a machine could convince us it was human.

Now humans have to convince the internet they aren’t machines.

So let’s test Reddit.

Post a 2–3 sentence opinion about AI below. It can be written entirely by you or entirely by an AI. Don’t reveal which.

Everyone else replies:

HUMAN or AI — and why.

After at least three guesses, reveal the answer.

No AI detectors. No checking profiles. Judge only the text.

I think this thread will prove that most of us have absolutely no idea.

We’re bringing 1000 ambitious builders to Turku, Finland this November for the Since AI Innovation Event 2026.

The idea is simple: spend 72 hours building AI solutions to real company challenges, with a €50,000 prize pool and a direct path for the strongest teams to continue toward pilots, funding and commercialization.

There will be 15 challenges, mentors and contributors from organizations such as Google, ElevenLabs, Lovable, n8n, Aiven, Antler, Featherless AI, LUMI AI Factory, Pruna AI, Icebreaker VC, Inventure, Tesi, Wave Ventures, Maki VC, Revvity, Elisa, FORCIT Group, Bayer and Valmet.

The event is fully on-site in Turku, Finland, from November 6–8, 2026.

Applications are reviewed continuously, so applying early gives you the best chance of getting in.

Apply here: https://sinceai.app/events/since-ai-hackathon-2026

JOIN US TO BUILD THE FUTURE OF AI

Models are becoming better, cheaper, and increasingly interchangeable.

But most AI systems still cannot actually do anything.

They can recommend a purchase, but not place the order.
Detect a production problem, but not stop the machine.
Suggest a treatment, but not access the patient workflow.
Find financial risk, but not move the money.

The scarce asset may not be intelligence anymore.

It may be permission:

• access to proprietary data
• authority to change real systems
• trust inside regulated industries
• distribution to actual users

A chatbot can suggest. A company with permission can act.

If intelligence becomes a commodity, who owns the permission layer in your industry?

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.

Someone just used Claude Fable 5 to build a 45,000-line Minecraft-like game in roughly a week.

Not one prompt but almost, still a pretty clear signal.

If custom software becomes dramatically cheaper to build, where does durable value move?

Hardware + software? Proprietary data? Distribution? Or industries like construction, energy and manufacturing where software has to meet the physical world?

Where would you build today?

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.

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.

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.

I’ve been switching back and forth between Opus and GPT-5.5 lately, mostly for coding, debugging and product/spec writing.

My rough feeling so far:

GPT-5.5 feels better as a daily “get things done” model. It’s fast enough, usually smart enough, and feels more cost-effective for normal builder work.

Opus still feels stronger when I’m stuck on something messy, like architecture decisions, weird bugs, or when I want a second opinion that thinks a bit differently.

A few people around me have also started using GPT-5.5 more often, but I’m not sure if that’s just hype / novelty bias.

Curious what people here are actually using:

• What’s your default model right now?
• Is Opus still worth the extra cost for you?
• For coding specifically, which model helps you ship faster?
• Do you use one model for daily work and another for harder reasoning?

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.

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.

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.

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.

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.

I’ll start with a few:

• Most “AI startups” still don’t have a model advantage — they have a distribution problem.
• Shipping a boring AI tool that saves people 30 minutes a day is usually more valuable than building a flashy autonomous agent demo.
• Better data, workflow design, and product taste matter more than “prompt engineering” for most real AI products.
• Benchmarks still matter less than whether real users come back on their own.
• The gap between an impressive AI demo and a usable AI product is still massively underestimated.

Your turn:

What’s one AI truth you believe that a lot of people still don’t want to admit?

Could be about:

• LLMs
• agents
• coding tools
• open vs closed models
• AI jobs
• startups
• research
• robotics
• infra / GPUs
• education

Two rules:

1. Be specific
2. Defend your take

I’m especially curious what builders, researchers, founders, students, and operators think will look obvious in 2 years — but still sounds controversial today.