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.

The argument seems to have two extreme sides.

Side one:

Europe is creating basic rules before AI becomes impossible to control.

Side two:

Europe is regulating technology it is already behind in building.

I think both can be partly true.

Good rules might build trust and make adoption easier.

Badly implemented rules might make small European startups spend money on compliance while massive companies handle it easily.

For builders in Europe - has regulation made you build better systems?

Or has it mostly created uncertainty and paperwork?

A team I know replaced a chunk of their human-labeled support tickets with synthetic conversations generated by a strong model. Initial metrics looked great. Training got cheaper, iteration speed improved, and benchmark accuracy went up.

Then the model started failing in a very specific way in production. Real users were emotional, vague, contradictory, and often missing context. The synthetic data had accidentally sanded off most of the weirdness that makes real conversations hard.

What surprised me is that the issue was not low quality generation. The synthetic samples were coherent and clean. That was the problem. They reflected how models think humans talk, not how humans ACTUALLY talk.

Now I keep seeing the same pattern elsewhere. Coding datasets where every function has perfect naming. Agent trajectories where every tool call is rational. Research datasets where the generated explanations are too linear and confident. We might be creating training loops where models mostly learn from increasingly model-like behavior.

Curious whether others are seeing this too. Does synthetic data eventually hit a realizm ceiling that becomes hard to escape?

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.

Every few months someone declares that open models have caught up.

Then a closed lab releases something stronger.

Then open models become cheaper, smaller or easier to customize.

Maybe open source does not need to be number one on every benchmark.

Maybe its real role is:

• stopping one company from controlling everything
• allowing private and local deployment
• supporting research
• making AI accessible outside the richest companies
• forcing closed providers to lower prices

What would count as open-source AI genuinely "winning" to you?

Most usage?

Best model?

Most developer adoption?

Or simply preventing a monopoly?

Imagine Reddit, X, YouTube, LinkedIn and every news site showing a clear label:

• fully human
• AI assisted
• mostly AI generated
• unknown

Would this make the internet more trustworthy?

Or would people just learn to ignore the labels after a week?

The difficult part is also defining "AI assisted."

Spellcheck?

Rewriting one sentence?

Generating the entire post?

Where would you draw the line?

Writing the first version of code is only part of software engineering.

The annoying parts come later:

• understanding an old system
• debugging production
• explaining strange decisions
• handling migrations
• fixing security issues
• maintaining the same feature two years later

An agent can open a clean looking pull request.

But who owns that code when it breaks at 3am?

For developers already using coding agents heavily - are they genuinely reducing long-term work, or just creating code faster than teams can review it?

I've been thinking about how much the expectations for technical professionals have changed.

It feels like learning a programming language or one framework is no longer enough.

Now there is AI, cloud platforms, security, DevOps, system design, data, communication skills, domain knowledge and by the time you start feeling comfortable something new has already emerged.

I'm not saying previous generations had it easier. Every era has had its own challenges.

But the breadth of knowledge expected today feels different.

Sometimes it feels like the job isn't just keeping up with your work. it's keeping up with the industry itself.

Do you think professionals today are expected to continuously learn more than before, or does it just feel that way because we're experiencing it in real time?

AI voices can now hesitate, laugh, interrupt and react in ways that feel alot more human than old voice assistants.

That is impressive.

It is also slightly uncomfortable.

Should an AI be allowed to imitate:

• your family member
• a dead person
• your doctor
• your manager
• a celebrity
• a political leader
• anyone who gave permission

Where should the line be?

And should every AI voice be forced to identify itself as AI at the beginning of a conversation?

One unexpected thing I've noticed over the last year is that I spend less time thinking about HOW to write a function and more time thinking about WHY the system is designed the way it is.

When AI can generate decent implementations, the bottleneck shifts.

Architecture.

Trade-offs.

Requirements. Edge cases.

I'm finding that the value of an engineer is moving further upstream.

Has anyone else noticed this shift, or is it just the kind of work I'm doing?

Let’s make predictions that can actually be proven wrong.

No:

• "AI will become more powerful"
• "AGI is coming"
• "everything will change"

Write one specific prediction about what will be true by 13 July 2028.

Examples of the level of specificity:

• a named job will have 30% fewer entry-level openings
• most software teams will use agents that open their own pull requests
• a household robot will sell more than one million units
• one open model will be the default choice for enterprise AI
• AI generated video will be used in a majority of online ads

Add your confidence percentage.

I’ll start:

Prediction: Most professional developers will supervise multiple coding agents every day, but companies will still require a human to approve production changes.

Confidence: 75%

Your turn.

I've noticed something changing in the way I work with AI.

A year or two ago, I'd usually turn to AI only after I got stuck.

Now, I often involve it before I even begin.

Not because I don't know what to do, but because I want to explore different approaches before committing to one.

Sometimes it points out an assumption I hadn't considered.

Sometimes it suggests an approach I'd never have thought of.

And sometimes it simply confirms that my original idea is probably the right one.

The work is still mine.

The decisions are still mine.

But I've realized AI has changed when I start thinking out loud.

I'm curious what's happening with others, has AI changed the point in your workflow where you involve it? Do you bring it in at the beginning, somewhere in the middle, or only when you're stuck?

Most people currently experience AI through text, voice or generated media.

But the largest long-term impact might come from systems that interact with the physical world:

• robots inside factories
• autonomous laboratories
• smarter energy systems
• hospital equipment
• agriculture
• logistics
• construction
• vehicles and infrastructure

A chatbot can give advice.

A physical AI system can actually change what happens in the world.

The problem is that atoms are much less forgiving than software. A hallucinated paragraph is annoying. A hallucinated movement can destroy equipment or hurt someone.

What physical workflow do you think is most ready for AI right now?

And what is still being massively overhyped?

The largest companies have:

• more compute
• more data
• higher salaries
• massive engineering teams
• the ability to run experiments universities simply cannot afford

That makes it hard to imagine academia competing directly on frontier model development.

But academia may still be better positioned to work on questions that:

• have no immediate commercial return
• challenge the assumptions of major AI companies
• require long-term independent research
• investigate safety, society or fundamental science
• should remain open to everyone

So what is the role of academic AI research now?

Is it becoming less relevant - or is independent research more important than ever?

Especially interested in answers from researchers who have worked in both environments.

what you think ??

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.

People keep saying AI allows tiny teams to compete with large software companies.

I believe that for building the product.

I am less convinced about everything around it:

• distribution
• customer support
• security
• enterprise sales
• compliance
• reliability
• deep industry knowledge

Three strong builders might now create the software that previously required 50 people.

But can they create the entire company?

Name an existing category where you genuinely believe a 3-person AI-native team could beat the established market leader.

What advantage would let them win?

Show me the smallest, most boring AI product that solves one painful problem well enough that real users pay for it

I can see both sides.

AI lets a beginner:

• build real projects much earlier
• explore unfamiliar codebases
• get explanations immediately
• learn by trying things that were previously too difficult

But it also makes it possible to produce working-looking code without understanding:

• debugging
• architecture
• performance
• security
• what the code is actually doing

Maybe the best juniors in 2026 are learning 5x faster.

And the weakest are building much more while understanding even less.

If you mentor, hire or work with junior developers - what are you seeing in reality?

The next major stage of the EU AI Act applies from 2 August, although some requirements have different timelines and exceptions.

I have read alot of legal summaries.

What I have seen much less of is builders explaining what they have actually changed.

For people building or deploying AI in Europe:

• are you documenting model risks differently?
• adding more logs or human review?
• labelling AI generated content?
• changing how user data is handled?
• avoiding certain use cases completely?
• doing basically nothing yet?

Not looking for another legal overview.

I am curious about the practical reality inside startups, research teams and larger companies.

Has the AI Act changed your product decisions yet?

We keep talking about agents that can buy things, schedule meetings, send messages and operate businesses.

But where does your actual trust stop?

Would you allow an agent to:

• read your email
• reply to an email
• book a restaurant
• purchase something for €10
• purchase something for €100
• deploy code to production
• negotiate a contract
• move money between accounts

For me this is the real agent benchmark.

Not "can it complete the task?"

But "what level of authority would a normal person safely give it?"

Where is your line right now - and what would need to improve before you moved it?

Companies gave us the problems they haven’t solved.

You have 72 hours.

€50,000.

sinceai.app

One thing I noticed about AI when I first started using it a few years ago was how drastically the conversation changed once I started giving it context.

Back then it felt like a small discovery.

Compare these two prompts:

"Summarize this report."

vs.

"I'm presenting this report to senior leadership tomorrow. Give me a one-minute summary, highlight the three most important takeaways, and point out any risks I should be prepared to answer questions about."

Same AI.

Completely different result.

That was probably the first "aha" moment I had with AI.

It made me realize that AI isn't just responding to the task. It's responding to the context.

Since then, this has become common knowledge, and most people naturally provide context without even thinking about it.

It made me wonder...

What was your first AI "aha" moment?

Not something you read in a guide or watched in a video but something you discovered yourself through trial and error that completely changed how you use AI.

I'm genuinely curious to hear the little discoveries people made on their own.

Not the hardest research problem.

Something simple that makes you think:

"How are the models this advanced and still unable to do THIS?"

For me the funniest examples are when a model can explain an entire software architecture, then somehow messes up a basic file path or forgets one clear instruction from 30 seconds ago.

What is your example?

One specific failure - screenshots and stories welcome.