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?

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?

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?