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TL;DR Prompt engineering optimised the wording of a single human-written request. Context engineering optimises the entire set of tokens in the model’s window across a whole run - system prompt, tool definitions, retrieved documents, tool results, conversation history, and memory The shift happened because of agents. The window is no longer one prompt you wrote - it is an accumulation that grows on every step, and most of it is produced by the system, not by you More context is not better context. Research on “context rot” and the older lost-in-the-middle effect show model accuracy degrades as the window fills, even well below the advertised limit The four levers are retrieval (what you pull in), memory (what persists across runs), tool results (what tools dump back), and compaction (what you summarise and discard) Treat the window as a budget. Measure its token composition, design tools to return terse output, curate rather than accumulate, and keep the static prefix stable so prompt caching still works For a few years, “prompt engineering” was the named skill of working with language models. It meant finding the wording, the framing, the few-shot examples, and the role instructions that coaxed the best answer out of a single request. It produced a small industry of prompt libraries, prompt marketplaces, and job titles. And in 2026 it is mostly gone, absorbed into something larger and harder. ...

TL;DR Start with What Actually Belongs in My AI Dev Stack in 2026 - the canonical stack essay Then An AI Tooling Learning Path - phased skill-building order Deep dives below cover comparisons and spec-driven workflows; single-tool posts are briefs, not entry points Canonical essays What Actually Belongs in My AI Dev Stack in 2026 An AI Tooling Learning Path: Logical Phases for 2026 Context Engineering - the production skill behind reliable coding agents Spec-Driven Development - when the brief becomes the product Deep dives Claude Code vs Cursor: A 6-Month Comparison GitHub Spec Kit and Spec-Driven Development GitHub Spec Kit in 2026: SDD Goes Mainstream My AI-Augmented Design Workflow When to Fine-Tune vs When to RAG Briefs (moment-in-time) These are useful snapshots, not the starting point: ...

TL;DR Cost is a design constraint, not an afterthought - model tier, context size, and deployment location are economic decisions Read the essays below in any order; start with Token Economics if you only have time for one Pairs with open-weight models and local inference guides Core essays Token Economics: Why the Cost of AI Isn’t Going Down GPU Servers vs AI API Credits: The Real Cost Breakdown Local AI vs Cloud AI: The Tradeoff Landscape in 2026 The AI Energy Crisis: Why Data Center Power Will Define the Next Decade Cerebras, Groq, SambaNova: The Inference Hardware Insurgents Adjacent The State of Open-Weight Models in 2026 - when open weights beat closed APIs on price Prompt Caching - the quiet latency and cost win The Token Efficiency Mindset - curating spend per conversation Is the $20 AI Subscription Era Over? We Are Learning to Buy Intelligence Related Reading AI Dev Tooling: A Reading Path for 2026 - canonical path for coding agents and stack decisions that depend on these cost constraints Home Agent Stack: From Mac Studio to Secured MCP Tools - building the hardware and software layer these economics govern Reasoning Models in 2026: What Changed and What Didn’t - why reasoning models carry a different cost profile than base models The Free Intelligence Era - the macro argument for where intelligence costs are headed

TL;DR Composer 2.5 is Cursor’s most capable in-house coding model yet, built on Moonshot’s open-source Kimi K2.5 checkpoint with about 85% of total training compute spent on Cursor’s own continued pretraining and RL The model is purpose-built for the agent loop inside Cursor - long-horizon tasks, hundreds of tool calls, multi-step instructions - rather than as a general-purpose chat model Cursor claims parity with Claude Opus 4.7 and GPT-5.5 on its own CursorBench v3.1 (63.2%) and a strong 79.8% on SWE-Bench Multilingual Pricing is dramatically lower: $0.50 / $2.50 per million input/output tokens on the default variant, with included usage doubled for the first week Together with SpaceXAI, Cursor is now training a much larger successor model from scratch on Colossus 2 with around 10x the compute - so 2.5 is a waypoint, not the endgame For a while, Cursor was an IDE wrapped around someone else’s models - Claude, GPT, Gemini. That story has shifted. With Composer 2.5, released this week, Cursor has shipped its most capable first-party coding model yet, and it is a serious enough piece of work that it deserves real consideration as a daily driver rather than a budget fallback. ...

A common dismissal of modern AI goes like this: “It is just a fancy autocomplete. It memorises text and stitches it back together. There is no real understanding, only retrieval.” It is a comforting story, and it has the shape of a critique that ought to be true. But spend enough time with frontier systems and a different picture starts to form. The thing that large models actually seem to be good at is not memorisation. It is something stranger and arguably more important: the formation of analogies, the combination of distant concepts, and the generation of conceptual relationships that were not explicitly present in any one place in the training data. ...

TL;DR This path walks through the full stack I run on a Mac Studio: local models → MCP tools → memory → remote access → security Almost no other blogs document the build and the hardening layer together Finish with Securing AI Agents before giving the agent real filesystem or mail access Part of the broader Trust series Read in order Which Mac Studio Should You Buy for Running LLMs Locally? - hardware and model sizing Giving Your Home AI Agent Real Tools: MCP Servers on a Mac Studio - wiring the tool layer Giving Your Home AI Agent Memory That Lasts - persistence across sessions How to Phone Your Home AI Agent - remote access when you are away Securing AI Agents - least privilege, confirmation gates, audit logs Adjacent guides Running AI Models Locally with Ollama - lighter-weight local inference option Agent Protocols in 2026: MCP, A2A, and ACP - the protocol layer Local AI vs Cloud AI - when to host vs call APIs DGX Spark vs Mac Studio - if you are sizing a dedicated inference box Related Reading AI Economics and Hardware: A Reading Path - token costs, GPU sizing, and energy constraints behind every hardware decision AI Dev Tooling: A Reading Path for 2026 - the coding and development layer that sits above the agent infrastructure Open WebUI: A Self-Hosted LLM Interface - web interface layer to pair with local inference Agent Protocols in 2026: MCP, A2A, and ACP - the protocol layer connecting agents to tools

The conventional reliability engineering toolkit was built for systems that behaved the same way each time given the same input. AI agents do not behave the same way each time given the same input. The classic tools - unit tests, integration tests, deterministic replay, traditional monitoring - all assume a property that the systems being operated do not have. This mismatch is not a small operational annoyance; it is the central challenge of running AI agents in production, and the patterns for handling it are still being worked out. ...

Dario Amodei is one of the few frontier-lab CEOs whose public talking points have not changed materially in five years. The same message he gave to small audiences in 2021 - that powerful AI is coming faster than people think, that the safety problem is real, and that the companies building it have an obligation to do so carefully - is the message he is giving to Congress and Davos in 2026. The thing that has changed is that he now runs the company most aggressively turning that message into a commercial position. ...

AlphaFold’s release in 2021 was the AI-for-science moment that broke through to the general public. A computational solution to a 50-year-old problem in biology - predicting protein structure from sequence - that produced a tool used by hundreds of thousands of researchers. The narrative around AI-for-science crystallised: deep learning would produce a series of similar breakthroughs across scientific domains. The 2026 reality is more interesting and less clean. AlphaFold-class breakthroughs have been rarer than the early narrative suggested. But AI has spread across scientific practice in subtler ways that, in aggregate, have done more to change how science is actually done than the few headline breakthroughs. ...

The open-weight model conversation in 2023 was about whether the open ecosystem could keep up with the frontier labs at all. The conversation in 2024 was about how big the gap was. The conversation in 2026 has changed shape: on most benchmarks that matter to most production workloads, the open-weight ecosystem has either closed or substantially narrowed the gap, and the strategic question is no longer “can we use open models” but “which open model fits this workload best.” ...