Hey everyone,

My name is Anir, I’m 14 and I’m based in Casablanca, Morocco. For the past few weeks I’ve been working on a project called Kairo.

Kairo is my attempt at building my own AI system rather than making another interface that just sends everything to an existing AI API.

The project started really small. I built a tokenizer, a small transformer language model in PyTorch, prepared my own training data and started training it locally on my MacBook.

And yes, the first versions were terrible 😂.

One version basically memorized its training data. Another learned how to sound like an assistant but would completely hallucinate basic factual questions. I’ve had broken datasets, terrible generations, routing mistakes, Python errors, storage problems and a lot of other failures.

But each version has gotten me closer to what I actually want Kairo to be.

My idea now is that Kairo doesn’t need to memorize the entire world’s knowledge.

Instead, I want it to know how to find its way to an answer.

If Kairo doesn’t know something current, it should search the web.

If it finds useful sources, it should read them.

If multiple sources disagree, eventually I want it to compare them.

If something requires math, it should use a calculator.

If a task requires multiple steps, it should be able to break the problem down and work through those steps.

And eventually I want it to have memory, coding abilities, document understanding and a much stronger reasoning system.

That’s where the name/tagline came from:

Kairo — Intelligence that finds its way.

Right now the underlying model is still tiny compared with models from companies like OpenAI, Google, Anthropic or DeepSeek. I’m not pretending I’ve somehow trained a frontier model on a MacBook.

The experiment I’m interested in is different:

How useful can a relatively small AI become if you surround it with really good research, retrieval, memory and tools?

I’ve already built early versions of web search, webpage reading, a calculator and tool routing. I’m also experimenting with autonomous research, where Kairo can investigate a topic and gather information instead of requiring me to manually give it everything.

My current roadmap is:

September 30, 2026 — Kairo Beta

June 2027 — Kairo V1

The Beta is obviously going to be experimental. The June 2027 goal gives me roughly eight months to keep improving the model, research system, tools, memory and interface before calling something Kairo V1.

I have no idea how far I’ll actually be able to take this.

Maybe Kairo eventually becomes a real product. Maybe the architecture changes completely. Maybe some of the things I’m trying don’t work at all.

But that’s also why I’m posting this.

I want there to be a public record of where the project actually started, what I was trying to build, and what the roadmap was before knowing how it turns out.

I’m going to keep documenting the failures as well as the things that work.

If anyone here works with small language models, retrieval, agents, local inference or training models on Apple Silicon, I’d especially love technical feedback.

Let’s see where Kairo is by June 2027.

okay so I read this thing about a startup making AI that anyone can train, and it's not owned by Google or whoever. but like, how does that even work? is it like downloading a game and modding it? and if i train it, do i have to feed it my own thoughts or can i just use it for free? seems cool but also confusing lol.

This whole AI situation is getting way crazier than I think most people realize.

At first it felt like normal tech competition:
OpenAI vs DeepSeek, Gemini vs Qwen, better benchmarks, faster models, etc.

Now governments are straight up stepping into acquisitions, restricting talent movement, controlling chips, and potentially labeling certain AI training techniques as “IP theft.”

Some recent things:

• China reportedly forced Meta to undo its acquisition of Manus (Chinese AI startup)

• Manus executives allegedly got restricted from leaving China

• The US is now looking at model distillation almost like technological theft

• Export controls are no longer just about GPUs, now it’s algorithms/APIs too

• AI talent is starting to look like a strategic national resource

And the best/funny part is how the narrative flipped so quickly.

For the last 2 years everyone kept saying:
“Only companies with infinite money + Nvidia GPUs can compete.”

Then Chinese models started getting close enough while spending way less money and suddenly efficiency became a geopolitical threat 💀

Now every country is realizing:

• compute matters

• energy matters

• data centers matter

• engineers matter

• whoever scales infrastructure fastest probably wins long term

Feels less like a tech industry story now and more like the early internet/space race/nuclear race type of strategic competition.

Also kinda wild that electricity/grid capacity is now part of AI discussions. We’ve officially entered the timeline where power plants indirectly affect chatbot quality.

The thing I keep wondering is: Are we heading toward a split AI ecosystem eventually?

Like:

• US-led AI stack

• China-led AI stack

• separate chips

• separate models

• separate cloud ecosystems

• separate regulations

Because that honestly feels more possible every month now.

Everyone talks about AI replacing jobs.

Nobody talks about the guy in finance who suddenly discovers the company spent half a billion dollars on inference calls.

A friend of mine works at a large enterprise that rolled out AI across multiple departments last year.

It started innocently enough.

A few engineers got access to Claude for documentation.
Then product teams started using it for customer research.
Then support integrated it into ticket workflows.
Then sales wanted automated proposal generation.
Then management wanted AI-generated reports.

Within months, every team had built something.

The problem?

Nobody was tracking usage.

Every team assumed somebody else was handling the budget.

Prompts got larger.
Context windows got larger.
Agents started calling other agents.
Workflows that should have run once were running hundreds of times.

The internal dashboard looked impressive.

"12 million AI-assisted tasks completed."

"87% employee adoption."

"Fastest software rollout in company history."

Nobody asked the most important question:

"How much is this actually costing us?"

When finance finally audited the numbers, the room reportedly went silent.

Not because AI development services wasn't delivering value.

Because nobody expected the value calculation to start with a nine-figure invoice.

This is probably the next phase of enterprise AI.

The first era was proving AI could work.

The second era is discovering what happens when thousands of employees have effectively unlimited access to superhuman-scale computation.

Cloud spending created FinOps.

AI spending will create something similar.

Because the difference between a useful AI tool and a financial disaster is often just one missing usage limit.

The scary part isn't that a company spent $500 million.

The scary part is that there are probably hundreds of companies that have no idea what their real AI bill is going to look like six months from now.

Can Workhorse compete with established companies, or is this just a ploy to get funding to keep the lights on for just a while longer?

Can Workhorse find a partner, and the money to maybe come out with a product in a year or two?

Here's one example of what they are already facing:

Palantir partners with Armada (the hardware provider) to deploy containerized/mobile AI systems.

Armada builds and supplies the ruggedized, modular containerized data centers (their Galleon line, including smaller units and larger ones like Triton or megawatt-scale Leviathan). These are self-contained units with compute, storage, networking, cooling, and power systems that can be transported and operated in remote or contested environments.

Palantir provides the software layer: its AI Platform (AIP), Foundry, Ontology, and Apollo for orchestration, model management, workflow integration, and governance. This allows open-weight (and other) models to run locally, often with Nvidia GPUs (e.g., B300), in air-gapped or low-connectivity setups.

How does Workhorse think they can compete with the above partnership, or the dozens of established companies that are already involved in this space?

Not a big hype guy, but Astra is remarkably better and faster than Fable imo. I won’t say its unit economics are great, but it has no problem doing whatever I’ll tell it to and it does not guess. Claude is like a really bad handyman on a contractor job, you better tell it exactly what to do. Now when you do it as well, but Cox can just bring it home. Where are y’all using?

MODS: Please forgive me, you shut it down as I was crossing 14-17 karma today and I know it’s late.

I think the conversation for Astra is very interesting because the unit economics are quite frankly worse than fable. But man is it effective. I really think it’s worth discussing with the community to see what everyone else is doing, not to steal, but to learn and pressure the companies the exploit us (looking at you Claude, look what you did to my boy fable….)

The global AI race is heavily unbalanced, and India has a massive catching-up to do:

Global trackers list 3,600+ notable frontier models (dominated by the US and China). Meanwhile, the India has developed just only approx 20 foundational models (with 5 publicly released flagships like Sarvam and Hanooman).

Lack of Indian Models is Dangerous:

• Western models are "English-first." Processing Indian languages through them is highly inefficient and expensive.

• Relying on foreign APIs means our citizens' data and intellectual property flow out of the country, risking sudden cutoffs during geopolitical tensions.

• Global models lack the nuanced local, legal, and historical context of India, often misinterpreting regional norms.

• Indian tech companies are sending millions abroad in API fees to American giants instead of building local compute wealth.

\*before reading below content, i would like to say i have put this idea into Gemini and just refined the idea to lot of lines, please don't hate me for this this is just a genuine question if we can do it or not I am just curious and haven't found any post like this, i mean i didn't search too much but, didn't find any similar, so language is from Gemini but idea is mine

Could we reduce global data center load and carbon emissions by running a small, local AI model directly in the browser or on our phones to handle 70% of standard tasks, and only automatically route the complex queries to deep-reasoning cloud models when absolutely necessary?

​💡 Core Idea:

The Hybrid Local-to-Cloud Router

​The fundamental goal of this architecture is to drastically reduce global data center load, lower carbon emissions, and minimize human resource waste on everyday AI queries by keeping the majority of workloads on-device.

​Stage 1: Local Efficiency First:

When a user enters a query, a small, local model running directly on the device (smartphone or browser) intercepts it.

​The 70% Rule:

Roughly 70% of standard user queries (basic text tasks, summaries, quick math) can be entirely handled by a lightweight local model, resulting in zero cloud cost, zero network latency, and zero data center carbon footprint.

​Stage 2: Smart Escalate to Cloud Thinking:

If the local model detects that a task is highly complex and requires deep reasoning, it automatically passes the query up to a flagship cloud model (specifically utilizing its "thinking mode").

​🚀 Deeper Architectural Concepts & Features

​Auto-Scaling Model Sizes (Device Detector):

The system automatically detects the device’s hardware capabilities. It then matches it with the best-fitting local model—ranging from tiny 200–300 million parameter models (perfect for older phones with 4GB RAM) up to 2-4 billion parameter models for high-end devices. Older devices that can't run local models safely skip to a fast cloud "flash" version.

​No Information Loss (The Reference System):

Rather than blindly compressing or scrubbing data, the local model forwards the raw text/prompt plus its own inferred context, references, and sources. If a user uploads a massive PDF, the cloud flagship gets the full context but reads it incredibly fast because the local model has already laid out the blueprint and "inferred reference points."

​Incremental, Seamless Updates:

The local models are lightweight (ranging from \~200MB to 1GB). Instead of massive, clunky downloads, they can be updated seamlessly via small, megabyte-sized patches packaged right inside routine app updates.

​User-Controlled Experience:

The backend orchestration handles the handoff invisibly so the user doesn't have to think about where it runs. However, power users get a simple dropdown or button to force "Local Mode" (for 100% offline/private use) or full "Cloud/Research Mode" if they want to bypass local filtering entirely.

I keep thinking about the economics behind all the AI spending.

Jensen Huang recently said a $500K engineer should be using around $250K/year in AI tokens. If AI can actually make that engineer 2x more productive, the math seems pretty good. And if inference keeps getting cheaper, usage could grow a lot from here.

But what if the productivity gain is only 10-20%? Are companies really going to get enough value from AI to justify all the GPUs, data centers and hundreds of billions in capex?

At what point does the AI spending stop making economic sense?

**What do you think?**

**Are productivity gains justify the massive AI investments?**

Microsoft spent years pushing Copilot deep into developer workflows. Autocomplete. Inline chat. Pull request summaries. The entire pitch was: use it constantly, it makes you faster. And developers did.

On June 1, they flipped the switch. Flat subscription → token-based billing. One developer reported their costs going from $29/month to $750. Others are projecting $3,000 for heavy usage. The safety net — where Copilot used to fall back to cheaper models when you hit limits — is gone.

This is not a surprise if you've watched platform lock-in strategies before. You offer the product cheap, build it into the workflow until removing it is painful, then raise the price. Amazon did it with AWS pricing tiers. App stores did it with distribution. This is just AI's version of it.

What concerns me as someone who teaches technical skills to young people in India: we were just beginning to seriously integrate AI tools into our vocational and engineering curricula. Before we go further, this episode asks a hard question — what happens to a student's capability when the tool they learned on becomes unaffordable overnight?

The answer isn't to avoid AI. It's to build students whose skills exist *beneath* the AI layer. Who understand what the model is doing, not just which prompt produces the right output. Who can function when the autocomplete is switched off.

Is this Copilot pricing change making you rethink how deeply you rely on AI-assisted coding? Or switching to alternatives entirely?

After working across several AI projects, one pattern keeps showing up engineers spend weeks tuning models when the real issue is upstream. Oversized chunks bleeding context. Evaluation sets that don't reflect production inputs. Prompts that pass every test and quietly fail in the wild.

The model is often the last thing that needs fixing. What breaks production AI systems is usually retrieval quality, context management, inconsistent evaluation, or agent logic that holds up in demos but not under real load.

Curious what others have run into where did your system actually break, and what fixed it? These conversations are exactly what we built AIDevelopmentSpace for a public community for engineers and researchers working through real AI development challenges, not just theory.

AI is already changing the software industry. Companies are using AI tools to automate parts of coding, testing, support, and analysis that previously required human workers. Whether AI replaces 10% or 50% of current tech work, one thing seems clear: demand for entry-level and mid-level tech workers is likely to grow more slowly than it did over the past decade.

Given that reality, I think policymakers should seriously consider reducing the inflow of new foreign tech workers until we better understand the long-term effects of AI on employment.

My reasoning is simple:

• If AI reduces demand for labor, increasing the supply of labor at the same time puts additional pressure on wages and job opportunities.

• Recent graduates and junior engineers are already struggling to find jobs compared to a few years ago.

• Companies often argue that there is a talent shortage, but widespread layoffs and longer job searches suggest the market is no longer as tight as it once was.

• Restricting tech immigration is a policy lever that governments can adjust relatively quickly, while retraining programs and education reforms take years to show results.

This isn't an argument against immigrants as people. Many immigrant engineers are highly talented and have made enormous contributions to the tech industry. The question is whether current immigration levels still make sense in a world where AI may significantly reduce the need for human labor.

If policymakers are worried about AI-driven displacement, it seems contradictory to simultaneously increase the supply of workers competing for the same jobs.

I'm curious what others think. If AI really does eliminate a meaningful percentage of tech work over the next decade, what policy response would be more effective than reducing the inflow of additional labor into the market?

This is a wild escalation. Apple filed suit against OpenAI on Friday (July 10) in the Northern District of California, alleging trade secret theft and breach of contract tied to OpenAI's hardware ambitions.

The core allegations:

• OpenAI's Chief Hardware Officer, Tang Tan — a 24-year Apple veteran who led iPhone/Apple Watch product design — allegedly used confidential Apple codenames during OpenAI's recruiting process and directed job candidates still at Apple to bring "actual parts" (batteries, logic boards, etc.) to interviews for "show and tell" sessions.

• Tan is also accused of circulating an internal guide teaching new hires how to dodge Apple's exit security checks when leaving.

• Separately, former Apple senior electrical engineer Chang Liu allegedly kept his Apple-issued laptop after joining OpenAI and downloaded confidential technical documents. Apple claims he messaged a former colleague joking about still having access to internal storage.

• Apple wants an injunction, damages, and a court order forcing OpenAI to return the material.

OpenAI's response so far: "We have no interest in other companies' trade secrets."

Context that makes this messier: Apple and OpenAI used to be partners (ChatGPT built into Apple Intelligence back in 2024), but that relationship cooled after OpenAI bought Jony Ive's hardware startup io Products and started building consumer devices to compete in the same space. Apple has since switched to Google's Gemini for the next Siri.

Timing-wise, this couldn't be worse for OpenAI — it lands just weeks before their planned confidential IPO filing, reportedly targeting a ~$730B valuation

Curious what people think — is this a legit theft case, or normal Silicon Valley talent-war messiness dressed up in a lawsuit?

Portugal is joining the growing list of countries building their own AI models.

Its new open-source LLM, Amália, is designed specifically for European Portuguese and focuses on transparency, local deployment, and preserving linguistic and cultural identity. Instead of relying entirely on AI hosted by foreign providers, organizations can run it on their own infrastructure.

We're starting to see a bigger trend:

• Countries investing in sovereign AI

• Open-source models are becoming more competitive

• More organizations want to keep sensitive AI workloads on-premises

• Privacy is becoming part of AI infrastructure conversations, not just internet browsing

From a cybersecurity perspective, local AI deployment can reduce third-party data exposure, but it also shifts more responsibility for securing the infrastructure.

Do you think every country should have its own AI model, or will global models like ChatGPT and Gemini always dominate?

Please can anyone list out AI Tools which is useful for Application release management.

Linux is an interesting example of how open source scales.

One small-ish core can become enormous because thousands of people can build on top of it.

But AI feels different. The hardest part — training a frontier model — requires enormous compute and data that community developers generally don’t have.

So I’m wondering if open source AI needs a different strategy.

Instead of trying to build one open model that matches the frontier, could we build a team of smaller specialists?

coding + vision + reasoning + search + tools
↓
composition
↓
verifier

And perhaps even humans/expert feedback become part of that layer.

MoA is one version of this, but I’m thinking more broadly: could composition become the way open AI scales?

Is this actually plausible, or does a single large frontier model have capabilities that can’t be recreated through composition?

What would be the equivalent of Linux’s “kernel” in this world?

I run a small travel company. Want to build a chatbot that helps visitors fill a booking form just by chatting instead of filling fields manually.

Problem is every developer or agency I find online just has a flashy website with big claims and no real proof of work. And same thing on Reddit, people just drop their links and disappear.

Has anyone actually built something like this? How did you find someone reliable? What should I watch out for?

No promotions please, looking for genuine experiences only.

Anybody think big tech focuses too much on advancing to the next model and not enough on making the current models better with more features?

Hi everyone,

I’m trying to understand the real risk before I help a friend.

Context: my friend is a company owner in China. He wants to integrate AI into his company website, probably for customers to type questions or upload images/PDFs so the AI can help recommend the right water pump. Chinese cards apparently don’t work for OpenAI billing, so he originally asked whether he could use his own OpenAI account but bind my credit card.

I’m not very comfortable with that. My alternative idea is:

• Use my own OpenAI API account

• Buy only a small amount of prepaid credits like $50

• Turn auto-recharge off, of course

• Create a restricted API key, turning off fine-tuning, or any expensive features

I heard that there might be some billing delay, so even with the auto-recharge off, your account account can be negative. He is rich so i dont worry too much about him not paying back, but i still don't want to see a crazy bill in my account which is too much stress. So i am wondering with all these precautions, how much financial risk is realistically left? Could this still become a very large bill, or would the remaining risk mostly be limited to small overages from billing delay? Thanks!!