📅 2026-Mar-10 ⬩ ✍️ Ashwin Nanjappa ⬩ 🏷️ linkblog ⬩ 📚 Archive
The $599 iPhone 17e, with the A19, benchmarks faster in single-core CPU performance than the $599 MacBook Neo, with the year-old A18 Pro.
This post also has a useful pricing comparison table for the iPhones on sale right now.
The MacBook Neo is not a footnote or hobby, or a pricing stunt to get people in the door before upselling them to a MacBook Air. It’s the first major new Mac aimed at the consumer market in the Apple Silicon era.
The Neo is a mass-market device that was conceived of, designed, and engineered to expand the Mac user base to a larger audience. It’s a design statement too, but of a different sort — emphasizing practicality above all else. It’s just a goddamn lovely tool, and fun too.
Delivering new code has dropped in price to almost free… but delivering good code remains significantly more expensive than that.
This post has a good definition of good code
.
Every good model understands
red/green TDDas a shorthand for the much longeruse test driven development, write the tests first, confirm that the tests fail before you implement the change that gets them to pass.
The most interesting lesson from the Claude C Compiler is not that AI can build a compiler. It’s how it built one. CCC didn’t invent a new architecture or explore an unfamiliar design space. Instead, it reproduced something strikingly close to the accumulated consensus of decades of compiler engineering: structurally correct, familiar, and grounded in well-understood techniques.
CCC shows that AI systems can internalize the textbook knowledge of a field and apply it coherently at scale. AI can now reliably operate within established engineering practice. This is a genuine milestone that removes much of the drudgery of repetition and allows engineers to start closer to the state of the art.
November was, for me and many others in tech, a great surprise. Before, A.I. coding tools were often useful, but halting and clumsy. Now, the bot can run for a full hour and make whole, designed websites and apps that may be flawed, but credible. I spent an entire session of therapy talking about it.
This Is Not The Computer For You
This computer is for the kid who doesn’t have a margin to optimize. Who can’t wait for the right tool to materialize. Who is going to take what’s available and push it until it breaks and learn something permanent from the breaking.
The reviews can tell you what a computer is for. They have very little interest in what you might become because of one.
Fantastic essay that compares the simple Casio F-91W with all the digital gadgets of today that need our constant attention.
Nothing you own is finished. Everything exists in a state of permanent incompletion, permanently needing. Your phone needs updates, needs charging, needs storage cleared, needs passwords rotated. Your apps need permissions reviewed, terms accepted, preferences re-configured after every update. Your subscriptions need evaluating, need renewing, need canceling, need justifying to yourself every month when the charge appears. The purchase isn’t the end of anything. It’s the first day of a relationship you didn’t agree to, with no clean way out.
Highlights from this Simon Willison interview is full of practical advise for vide coding and agentic engineering
I use the term agentic engineering to describe the practice of developing software with the assistance of coding agents.
What are coding agents? They’re agents that can both write and execute code.
What’s an agent? Agents run tools in a loop to achieve a goal.
A coding agent is a piece of software that acts as a harness for an LLM, extending that LLM with additional capabilities that are powered by invisible prompts and implemented as callable tools.
Coding agents are designed with this optimization in mind - they avoid modifying earlier conversation content to ensure the cache is used as efficiently as possible.
The defining feature of an LLM agent is that agents can call tools. A tool is a function that the agent harness makes available to the LLM.
Coding agents usually start every conversation with a system prompt, which is not shown to the user but provides instructions telling the model how it should behave.
Reasoning, sometimes presented as thinking in the UI, is when a model spends additional time generating text that talks through the problem and its potential solutions before presenting a reply to the user.
LLMs are restricted by their context limit - how many tokens they can fit in their working memory at any given time. These values have not increased much over the past two years even as the LLMs themselves have seen dramatic improvements in their abilities - they generally top out at around 1,000,000, and benchmarks frequently report better quality results below 200,000.
Carefully managing the context such that it fits within those limits is critical to getting great results out of a model.
Subagents provide a simple but effective way to handle larger tasks without burning through too much of the coding agent’s valuable top-level context. When a coding agent uses a subagent it effectively dispatches a fresh copy of itself to achieve a specified goal, with a new context window that starts with a fresh prompt.
Online visualization tool to view the tokens and token IDs of any input text.
When you behold the prompt file of a coder using A.I., you are viewing a record of the developer’s attempts to restrain the agents’ generally competent, but unpredictably deviant, actions.
If you want to put a number on how much more productive A.I. is making the programmers at mature tech firms like Google, it’s 10 percent, Sundar Pichai, Google’s chief executive, has said.
Claws
Clawis becoming a term of art for the entire category of OpenClaw-like agent systems - AI agents that generally run on personal hardware, communicate via messaging protocols and can both act on direct instructions and schedule tasks. It even comes with an established emoji 🦞
Taking a cursory look at the network waterfall for a single article load reveals a sprawling, unregulated programmatic ad auction happening entirely in the client’s browser. Before the user finishes reading the headline, the browser is forced to process dozens of concurrent bidding requests to exchanges like Rubicon Project (fastlane.json) and Amazon Ad Systems.
The background invisible pixel drops and redirects to doubleclick.net and casalemedia help stitch the user’s cross-site identity together across different ad networks.
Coding still matters. But increasingly, it’s the part machines can help with. More important now is judgment: the ability to choose the right problems, make sound architectural decisions, and direct both humans and agents toward meaningful outcomes.
It worksis easy.It will keep working in productionis much harder.
2 principles for good team velocity in code reviews:
Reviewers are expected to respond in a timely manner when it is their turn. If you don’t plan to respond within ~24h, then you should either remove yourself from the attention set or you should at least send a clarification message to the change owner.
Change owners are expected to manage the attention set of their changes carefully. They should make sure that reviewers are only in the attention set when the owner waits for a response from them.
An alternate perspective.
I’m utterly content to wait until their hype has been realised. Why should I invest in learning the equivalent of WordStar for DOS when Google Docs is coming any-day-now? If this tech is as amazing as you say it is, I’ll be able to pick it up and become productive on a timescale of my choosing not yours. It is 100% OK to wait and see if something is actually useful.
Fantastic gadget to use with all the USB cables at home and work to learn about the myriad details of USB and to separate the good cables from the bad.
Technology Connections gem uses the car engine oil pressure warning to delve into the workings of the internal combustion engine, engine block, pistons, crankshaft and oil pump.