Meta Platforms reported $201 billion in revenue in 2025. Net income was a record. Free cash flow was a record. The stock closed the year at an all-time high.
On Thursday, the company announced it is laying off 8,000 people.
Those two things are not a contradiction anymore. They are the business model. — Read More
Recent Updates Page 11
HX Is The New UX: What You Need To Know About Harness Experience.
For thirty years, the central obsession of product design has been a single question: how do we make it easier for a human to click the right button? We built funnels. We A/B tested button colors. We agonized over empty states and loading spinners.
That era is ending — not gradually, but structurally.
… Agents don’t navigate UIs. They negotiate with systems. And when the agent is the primary “user” of software, the human behind it occupies an entirely different role — one for which we have almost no design vocabulary. Until now.
HX — Harness Experience — is the design discipline governing the interface between a human and their agentic fleet. — Read More
The New Rules of Making Money With AI Tools
The direct answer: Making money with AI in 2026 is less about using AI and more about what you build around it. The tools are commodities. The real income lives in specialization, human judgment layered on top of automation, and solving problems so specific that generic AI can’t touch them. Operators who understand this are pulling ahead. Everyone else is racing to the bottom.
The honeymoon phase is over. Remember 2023? Selling prompt packs on Gumroad, flooding Amazon KDP with AI-written books, spinning up generic chatbot wrappers, and calling it a SaaS. For a hot minute, novelty was enough. Those days are gone — not because AI got worse, but because everyone caught up.
The barrier to entry dropped to zero. Which means zero is also what most low-effort AI hustles are now worth.
But here’s the thing nobody tells you: the real money was never in the tools. It was always in the thinking behind them. The AI landscape just finally sorted itself out enough to prove it. — Read More
Can AI Attack the Cloud? Lessons From Building an Autonomous Cloud Offensive Multi-Agent System
The offensive capabilities of large language models (LLMs) have until recently existed as theoretical risks – frequently discussed at security conferences and in conceptual industry reports, but rarely discovered in practical exploits. However, in November 2025, Anthropic published a pivotal report documenting a state-sponsored espionage campaign. In this operation, AI didn’t just assist human operators – it became the operator, performing 80-90% of the campaign autonomously, at speeds that no human team could match.
This disclosure shifted the conversation from “could this happen?” to “this is happening.” But it also raised practical questions: Can AI actually operate autonomously end-to-end, or does it still require human guidance at each decision point? Where do current LLM capabilities excel, and where do they fall short compared to skilled human operators?
To answer these questions, we built a multi-agent penetration testing proof of concept (PoC), designed to empirically test autonomous AI offensive capabilities against cloud environments. — Read More
A Hundred Robots Are Running A Bio Lab
The small robot has brushed past me five times in the last hour.
It runs loops around the perimeter of the third floor of this bio lab, serving as a courier. The machine’s job is to visit workstations and keep other robots – arms bolted to lab benches – fed with whatever they need be it pipette holders, sealed plates or something in a labeled bag. The little bot is relentless and unconcerned about me or much else beyond its job. Out of the corner of my eye, I spot chairs still rotating slowly on their bases from where it clipped them on the last pass.
About a hundred robotic arms fill this room, each one positioned beside a different scientific tool. The arms must deal with centrifuges, incubators, chambers and tubes. They run simultaneously and continuously. The small robot links them together, ferrying consumables between stations the way a junior scientist carries things between benches. Except the benches are robots. And so is the assistant. — Read More
From Vibe Coder to Product Builder
The lines between product management and software engineering are becoming increasingly blurred. As product managers, we can now show rather than tell; build rather than write. There’s a spectrum here.
… A lot of product managers stop at Bolt or Lovable – and that’s fine for visualising an idea. But I believe there’s a meaningful difference between visualising a product and actually building one. My take is that there are different degrees of product building, and if you want to move from prototyping ideas to shipping real products, you need to start using coding agents and get comfortable with some engineering basics. Not to become an engineer, but to get the most out of the tools. — Read More
The AI Chasm
Every week I see another LinkedIn post about how AI is going to transform everything. Another “X is dead” announcement and someone shipping their latest vibe coded project.
And don’t get me wrong. I get it. The hype is real.
My head isn’t buried in the sand. It doesn’t have the same Amazon Alexa and NFT vibes. This is more like the internet or mobile phones for sure.
But think about how long both of those took to take off?
… I want to give you a different perspective to the AI rhetoric.
A more balanced view that you might disagree with – claiming “but this time it’s different’ – or agree with.
Either way I want to hopefully give you a different perspective on everything that is happening right now. One that’s based in research and what’s historically happened before. — Read More
A good AGENTS.md is a model upgrade. A bad one is worse than no docs at all.
We pulled dozens of AGENTS.md files from across our monorepo and measured their effect on code generation. The best ones gave our coding agent a quality jump equivalent to upgrading from Haiku to Opus. The worst ones made the output worse than having no AGENTS.md at all.
That gap was surprising enough that we built a systematic study around it.
What we found: most of what people put in AGENTS.md either doesn’t help or actively hurts, and the patterns that work are specific and learnable. — Read More
Building the 11 Layers of a Production-Grade MCP Server + Agentic System
MCP servers are becoming the core focus of production agentic systems because they are where all the hard problems actually live: multi-tenant isolation, auth, rate limits, audit trails, and approval gates for destructive operations. Without them, agents leak data across tenants, burn budgets in runaway loops, and commit to refunds no human approved. An MCP server solves this by sitting between the agents and the data layer as a single secure tool surface, turning every agent call into an authenticated, policy-checked, rate-limited, audited operation before it touches a single row …
In this blog, we are going to build Atlas-MCP, a production-grade MCP server organized around twelve components that keep showing up on the 3 AM pager when teams skip them. On top of the server, we are also going to build a four-agent support copilot (Planner, Retriever, Synthesizer, Critic) that uses the server’s tools to answer real customer support tickets end to end. — Read More
Challenges and Research Directions for Large Language Model Inference Hardware
Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI trends, the primary challenges are memory and interconnect rather than compute. To address these challenges, we highlight four architecture research opportunities: High Bandwidth Flash for 10X memory capacity with HBM-like bandwidth; Processing-Near-Memory and 3D memory-logic stacking for high memory bandwidth; and low-latency interconnect to speedup communication. While our focus is datacenter AI, we also review their applicability for mobile devices. — Read More
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