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
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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
Mythos on Discord
Anthropic said Mythos was too dangerous to release. Then four random guys in a Discord gained access on day one by guessing the URL… — Read More
YouTube expands its AI likeness detection technology to celebrities
YouTube is expanding its new “likeness detection” technology, which identifies AI-generated content, such as deepfakes, to people within the entertainment industry, the company announced on Tuesday.
The technology works similarly to YouTube’s existing Content ID system, which detects copyright-protected material in users’ uploaded videos, allowing rights owners to request removal or share in the video’s revenue.
Likeness detection does the same, but for simulated faces. — Read More
GPT Image Generation Models Prompting Guide
OpenAI’s gpt-image generation models are designed for production-quality visuals and highly controllable creative workflows. They are well-suited for both professional design tasks and iterative content creation, and support both high-quality rendering and lower-latency use cases depending on the workflow.
… This guide highlights prompting patterns, best practices, and example prompts drawn from real production use cases for gpt-image-2. It is our most capable image model, with stronger image quality, improved editing performance, and broader support for production workflows. The low quality setting is especially strong for latency-sensitive use cases, while medium and high remain good fits when maximum fidelity matters. — Read More
Introducing the Agent Readiness score. Is your site agent-ready?
The web has always had to adapt to new standards. It learned to speak to web browsers, and then it learned to speak to search engines. Now, it needs to speak to AI agents.
Today, we are excited to introduce isitagentready.com — a new tool to help site owners understand how they can make their sites optimized for agents, from guiding agents on how to authenticate, to controlling what content agents can see, the format they receive it in, and how they pay for it. We are also introducing a new dataset to Cloudflare Radar that tracks the overall adoption of each agent standard across the Internet. — Read More
Agents are starting to operate real systems — who’s actually in control?
AI agents have moved quickly from copilots to economic actors faster than the infrastructure around them.
While agents now execute tasks and transact, they still lack standardized ways to prove who they are, what they’re authorized to do, and how they get paid across environments. Identity doesn’t travel, payments aren’t yet programmable by default, and coordination happens in silos.
Blockchains address this at the infrastructure layer. Public ledgers give every transaction a receipt that anyone can audit. Wallets give agents portable identity. Stablecoins are an alternative settlement layer. These aren’t future primitives. They work today, and they can help agents operate permissionlessly as real economic actors. — Read More
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