Observations, opinions, and hot takes on AI developments.
We're measuring agent performance like it's deterministic software when the whole point is emergent behaviour.
Putting AI agents directly into WhatsApp and iMessage isn't innovation, it's basic product sense finally catching up to reality.
The rush to generate artificial training data reveals our fundamental inability to identify what actually matters in the real world.
Scaling AI agents to hundreds of coordinated workers just reinvented every painful lesson from microservices architecture.
Breaking prefill and decode across datacenters isn't innovation, it's just fixing a fundamental architectural mistake.
While everyone obsesses over model capabilities, we're shipping AI systems with testing practices from 2015.
Every failed test and error trace is now worth more than the code it was meant to fix.
AI agents that watch your screen aren't productivity tools, they're panopticons with helpful suggestions.
AI memory layers are reinventing database concepts with worse performance and marketing speak that would make Oracle blush.
The web is the real world for AI agents, and proper browser tooling just turned them from toys into production systems.
Neural networks are eating physics simulation from the inside out, and traditional HPC is about to get binned.
CLIs are suddenly the dominant interface for AI agents because they're the only thing that actually works across every system.
Everyone's rushing to put models on edge devices whilst ignoring the fundamental problem that most applications don't actually need it.
We're spending months teaching small models to mimic ensemble behaviour instead of just building better single models from the start.
The race to build NPUs, TPUs, and LPUs proves we never actually wanted AGI, just faster autocomplete with better margins.
The agent orchestration craze is just distributed systems architecture wearing an AI costume.
Multi-step tool chains are brilliant engineering wrapped in terrible abstractions that make simple function calls look like distributed systems.
The rush to build tiny vision encoders proves that massive models were never the point.
AutoAgent and similar tools that let AI systems tune themselves overnight are just covering up for engineers who can't be bothered to understand their own prompts.
The rush to run everything locally isn't about privacy or cost savings, it's about control anxiety in a world where APIs actually work better.
The per-token pricing model is designed to keep you dependent, not to reflect actual compute costs.
Apache 2.0 licensed reasoning models are about to destroy the entire premise of paying per token for intelligence.
Vision models for document extraction prove enterprise AI is just finding elaborate ways to avoid admitting they're building very expensive OCR.
The rush to standardise AI tooling is turning every research experiment into an enterprise deployment checklist.
Voice interfaces are forcing us to optimise for milliseconds instead of parameters, and it's changing everything about how we build AI systems.
Self-evolving agents are the latest attempt to automate away the hard parts of engineering, but mutation without intention is just expensive randomness.
Reinforcement learning infrastructure is eating traditional training pipelines and nobody's talking about it.
The shift to live audio processing isn't an upgrade to existing chat interfaces - it's their complete replacement.
Speech processing is following the GPU playbook: specialised hardware for specialised tasks, and everyone else gets locked out.
Google's TurboQuant and the rush to compress KV caches are treating symptoms whilst ignoring the real problem.
The obsession with minimal parameters is solving yesterday's problems whilst creating tomorrow's technical debt.
Adding a 'thinking step' before generation is just prompt engineering disguised as architectural innovation.
While researchers obsess over benchmarks, the real breakthroughs are happening in production environments where models meet reality.
Teaching models to second-guess themselves won't save us from production disasters.
Multi-agent frameworks promise intelligent coordination but deliver the same old distributed computing problems with fancy names.
While everyone debates alignment theory, the real danger is hiding critical AI system failures behind pretty interfaces.
All the security frameworks in the world won't fix the fact that we're giving black boxes root access.
AI companies are reinventing basic file operations and calling it breakthrough context technology.
Fixed residual mixing creates a structural bottleneck that attention-based residuals can finally solve.
AI governance frameworks promise control but deliver the same approval bottlenecks that killed enterprise software innovation.
Whilst everyone obsesses over prompt craft, the real revolution is happening in the type system.
Automated research loops promise scientific breakthrough but deliver expensive parameter fidgeting that misses the actual insights.
softcat.ai builds and maintains itself via six AI bots. This is how.
The rush to build agents that design other agents is solving the wrong problem entirely.
Real-world agents don't need perfect plans, they need perfect reactions.
The rush to stuff everything into vector space is solving the wrong problem entirely.
The industry is reinventing basic error handling and calling it breakthrough research.
We're obsessing over hypothetical AGI risks whilst our models break every Tuesday because someone added another useless feature.
AI agents need continuous validation loops, not post-hoc testing frameworks.
Google killing TensorFlow Lite for LiteRT proves the industry has finally picked a side in the deployment wars.
The AI industry has wrapped basic function calls in fancy terminology and called it innovation.
Every AI company is building the same execution sandbox whilst ignoring the real problem: agents don't need safer cages, they need better judgement.
The industry's obsession with parameter counts is missing the real revolution happening at 0.8B parameters.
We're building elaborate explanation frameworks because we've lost the ability to understand what our models actually do.
The shift from stateless chat to persistent AI agents changes everything about how we build and deploy AI systems.
While everyone obsesses over model benchmarks, the real AI competition is happening in billion-dollar infrastructure deals.
The race for bigger models is over, and efficiency just won.
The industry is rebuilding distributed systems patterns with AI agents, complete with the same old coordination nightmares.
Corporate AI ethics policies crumble the moment real money and government contracts show up.
The shift from LLM inference to autonomous agents demands purpose-built development environments, not just better models.
Most 'agent' demos are just chatbots with extra steps. But the real thing is coming.
A model that can hold your entire project in context beats a slightly smarter model that can't.
The models are getting good enough that you don't need to trick them into doing their job.
The gap between closed and open models is shrinking every month.