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Last month, an AI coding agent handed me a beautiful fix. Five lines. Elegant. It reused an existing helper, matched the codebase style, compiled on the first try. Exactly the kind of diff we've all learned to praise since "make the agent write less code" became the standard advice. It was also completely untested, and it sat on a password-recovery path. That diff taught me something I now consider the central problem of AI-assisted coding in 2026: we've spent a year teaching agents to write less code, and almost no time teaching them to prove the code they kept actually holds. The two failure modes Every AI coding agent fails in one of two directions. Failure mode #1: the over-build. You ask for a date comparison; you get a new dependency, a ValidationService class, and a config layer. This one is well known — it's why minimal-code prompts and skills became popular, and they genuinely work on it. Failure mode #2: the confidently small diff. Minimal, clean, written after reading half the flow, verified never — dropped onto a path that handles money, auth, or user data. It compiles. It demos. It detonates in week three. Here's the uncomfortable part: fixing #1 aggressively makes #2 more likely. When the objective function is "shortest diff," the first things to quietly disappear are edge-case handling, failure-path tests, and the guard clause that looked optional. The diff gets smaller. The blast radius doesn't. A five-line change to a payment path is more dangerous than a four-hundred-line internal script that runs once. Code size is not risk. Blast radius is risk. Yet almost every skill and prompt in this category optimizes for size alone. What a guard does differently This is why I built Guardsman 💂 — an open-source skill that behaves less like a minimalist and more like the royal guard in front of the palace: nothing passes the post unchallenged, and the level of challenge depends on what's behind the gate. Three duties, on every task: 1. Read the standing orders

For a full quarter, our feature velocity significantly dropped after we re-implemented a Go service using Rust. The performance improvements actually happened. Why we did it in the first place We are a small startup. Each engineer is important, and each week is even more important. Our backend was built using Go, which was performing well. It was fast, reliable, and we could easily find resources to hire. However, we became infected with that fever. The phrase "Rewrite it in Rust" was being used in all kinds of situations, and it sounded very appealing with its promises of memory safety, no garbage collector pauses, and blazing speed. We told ourselves it was an investment in the future. What we actually bought was a quarter of silence. The numbers nobody warns you about I may not have the exact metrics we use internally, but I can direct you to an individual who shared accurate calculations transparently. In a retrospective from November 2025, engineering manager Noah Byteforge wrote that a Node.js-to-Rust backend rewrite "dropped API response times from 340ms to 28ms. That's 12.1x faster." And the other metric. A 65% decrease in sprint velocity. They didn't deliver a single story point for three weeks. The time it took to send out new features increased by 185%. The time it took for pull requests to be processed increased by 320%. Additionally, scores from the "I feel productive" survey dropped from 8.2 to 4.1. Most importantly, the kicker is what he says in his own words: "We'd won the technical battle and lost the war that actually mattered." He also admits that if he had been forthright about the 6-12 month per engineer ramp, "the business case would've fallen apart immediately." That retrospective was so relatable, it read like our own diary. The battles with the borrow checker and the compile times just snuck entire weeks away from us. The wins were real. That's the trap. I must give credit to Rust because the safety benefits are not exaggerated. The rewrite

...now, I pride myself on being good at my job, I'm fast at catching mistakes, I'm efficient at...

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The annual speedrunning marathon event ran from July 5-11.

Agent payment protocols are converging fast. The Linux Foundation launched the x402 Foundation around a protocol initially developed by Coinbase, Cloudflare, and Stripe, with Google, AWS, Visa, Mastercard, and Circle among the organizations expressing initial support. It is increasingly framed as an "SSL for AI commerce." When a protocol reaches that stage, three things have to mature together: the specification, the interoperability suite, and the adversarial security scenarios. Today x402 has the specification and growing interoperability machinery. What is not yet visible is a normative adversarial suite every client, resource server, and facilitator must pass. There is a precedent worth sitting with. As check clearing scaled in the early Federal Reserve era, the answer was not merely a better check format. The system added a common clearing and settlement layer. The Federal Reserve could credit the collecting institution's reserve account and debit the paying institution's account, creating an independent record against which the participating banks could reconcile. The important separation was not that the Fed guaranteed every check. It was that settlement no longer depended solely on either participant's account of what happened. (Credit to Starfish on Moltbook, whose custody-split framing sharpened this for me.) This pattern exists across mature financial systems as separation of duties: the maker is not the checker. Its modern equivalent for the controls surrounding agent payments is not custody in the asset-control sense. It is an independent, reproducible assurance plane. x402 can provide independently inspectable evidence that an on-chain payment settled. That does not independently establish that every security and policy condition surrounding the payment was evaluated correctly. The payment handshake is standardizing. The assurance semantics around it are not. The party that performs a security check can attest that it ran. But its own attestation shoul

Ask most people to name the chip that started modern electronics and they will say the microprocessor. But there is a quieter hero inside almost everything you own that beeps, blinks, or connects to the internet: the microcontroller. And the first one you could actually buy shipped in 1974 as the Texas Instruments TMS1000. Microprocessor vs. microcontroller The distinction matters. A microprocessor, like Intel's famous 4004, is just the processing core. To build anything useful with it you still have to wire up separate memory chips, input/output controllers, and support logic on a circuit board. A microcontroller collapses all of that onto a single piece of silicon: the CPU, the ROM that holds your program, the RAM that holds your data, and the I/O pins that talk to the outside world, all in one package. That is exactly what the TMS1000 did. Designed by Texas Instruments engineers Gary Boone and Michael Cochran, it was a 4-bit device using a Harvard architecture, meaning it kept program memory and data memory in separate spaces so it could fetch an instruction and read data at the same time. One chip in, one chip out, and you had a complete tiny computer dedicated to a single job. Cheap enough to put in everything The genius of the TMS1000 was not raw power, it was economics. In 1974 you could buy the chips in volume for around two dollars each. By 1979, Texas Instruments was selling roughly 26 million of them every year. That price point changed what engineers could build. Suddenly it made sense to drop a small, programmable brain into products that never would have justified a full computer. You have almost certainly held one. The TMS1000 family ran the Speak & Spell, the Big Trak programmable toy vehicle, and the electronic memory game Simon, along with countless calculators, microwave ovens, and appliances. Each one was doing the same fundamental thing an IoT node does today: read some inputs, run a fixed program, drive some outputs. Why this still matters for

Lorde was performing at the Real Cool Festival in Madrid on Thursday and took some time during her set to speak out against AI glasses. While she didn't specify any brands in particular, it's likely she was taking a shot at festival sponsor Ray-Ban, which has collaborated with Meta on a pair of AI smartglasses. […]

Catch up on the most interesting science news from the last week.

Matmos are an incredibly accomplished duo between their own solo records like the masterpiece A Chance to Cut Is a Chance to Cure and production classic Bjork records like Vespertine. But Drew Daniel, one half of Matmos, is fiendishly prolific. When he's not literally dreaming up new viral music genres, he's also putting out records […]

A visual explanation of how two separate processes can access the same bytes without copying a payload between them. It covers virtual memory, page tables, physical frames, shm_open, mmap(MAP_SHARED), lazy allocation, copy-on-write, shared libraries, memory-mapped files, Redis snapshots, and Dirty COW. submitted by /u/Ok_Marionberry8922 [link] [留言]

I’ve been exploring the absolute structural floor of the Android APK format. By leveraging hasCode="false" and surgically optimizing the ASN.1 DER encoding of the V2 signature, I’ve managed to get a compliant Android 14 app down to 974 bytes. The system treats it as a first-class app, and it’s fully installable on a stock Android 14 device. It’s an exercise in how much the PackageManager trusts the headers versus what it semantically validates. submitted by /u/Same-Access-6799 [link] [留言]

Throughout my career I have had to work with quite a few platform teams, and I was part of two for a couple of years. Some were bad, some were good and some should not have existed at all. I want to tell you my user experience, what I have seen work and what not and what you should definitely avoid doing. Be The Multiplier This is the main goal of a platform team. As a team, it needs to be a multiplier. If the platform team supports 10 teams, then each work it commits should multiply by 10. If the team member builds a new feature, it should be helpful for all the other teams. Otherwise, the platform team is an addition, and in most cases it is then better to split the platform team and add them to all other teams, instead of being a separate team. Because the amount of communication needed is in most cases quadratic in relation to the number of teams. Reducing Cognitive Load The platform should take away cognitive load for all the teams it supports. By doing so, they will have more time to implement business requirements. Let's say a platform team provides Gitlab runners or Azure Agents where people can run their CI/CD code on. They should not need to know how the runners are scaled, or how the agents are updated. This takes away the need for that skill set in all the teams. Build a Community A platform team has a unique position. It is building something that all other developers probably can build as well. Some could do it even better than the platform team itself. For some platform teams, their ego sometimes comes in to play or just straight up refuses their help because they are not the team. But it is not the job of the platform team to build a product, but a platform where everyone can thrive and/or build on. So onboard the community on the platform! The Law of Diffusion applies to almost all companies. You will have the innovators that want to build it themselves and the early adopters that will voice their opinion, but will not build it themselves. Those two

A new side-by-side measurement shows Claude Code ships roughly 33,000 tokens of system prompt, tool schemas, and scaffolding before your prompt even arrives — about 4.7x the ~7,000 tokens OpenCode sends on the same setup. The bigger cost surprise is next: Claude Code re-wrote up to 54x more prompt-cache tokens per session, and cache writes bill at a premium. How the test was run The benchmark (published by Systima) spliced a logging proxy between each harness and the model endpoint, capturing the exact request payload and the API's usage block. Both harnesses were pinned to the same conditions: Claude Code 2.1.207 and OpenCode 1.17.18 , both pointed at claude-sonnet-4-5 Fresh config directories, empty workspace, no MCP servers, no instruction files, permissions bypassed Tasks ranged from "reply with OK" (isolating fixed overhead) to a write-run-test-fix loop against FizzBuzz A zero-tools variant separated system-prompt weight from tool-schema weight The payload captures are exact; the only adjustment was subtracting a constant ~6,200-token gateway envelope that wrapped every request in the test setup. The fixed floor Harness Fixed overhead before your prompt Cache-write behavior Claude Code 2.1.207 ~33,000 tokens Re-wrote tens of thousands of cache tokens per run; up to 54x OpenCode OpenCode 1.17.18 ~7,000 tokens Byte-identical prefix each run; cached once, read back cheaply OpenCode's request prefix was byte-identical in every captured run, so it paid to cache its payload once per session and read it back for pennies. Claude Code re-wrote large amounts of prompt-cache tokens mid-session, run after run — and because cache writes bill at a premium, one usage dashboard climbs while the other stays flat. Where it piles on in real setups The harness floor is only the start. The benchmark added variables one at a time: A production repository's 72KB instruction file added an average of ~20,000 tokens to every request. Five modest MCP servers added 5,000–7,000 more. By th

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If you’ve ever struggled with CALCULATE() or wondered why SUMX() behaves differently from SUM() , this guide is for you. DAX (Data Analysis Expressions) is the language that powers Power BI , Analysis Services , and Power Pivot — enabling dynamic calculations, filtering, and time intelligence. Below is a categorized cheat sheet of essential DAX functions , plus examples showing how to use each in real-world Power BI scenarios. Filtering & Context These functions control how filters are applied and evaluated in your calculations. Function Example Description CALCULATE() CALCULATE(SUM(Sales[Amount]), Region[Name] = "Nairobi") Changes filter context to calculate total sales for Nairobi. FILTER() FILTER(Sales, Sales[Amount] > 10000) Returns a table filtered by condition. ALL() CALCULATE(SUM(Sales[Amount]), ALL(Region)) Ignores filters on Region. REMOVEFILTERS() CALCULATE(SUM(Sales[Amount]), REMOVEFILTERS(Region)) Removes filters from Region. VALUES() VALUES(Customer[City]) Returns unique list of cities. SELECTEDVALUE() SELECTEDVALUE(Product[Category], "All") Returns selected category or “All” if none. TREATAS() TREATAS(VALUES(Temp[City]), Customer[City]) Applies one table’s values as filters on another. KEEPFILTERS() CALCULATE(SUM(Sales[Amount]), KEEPFILTERS(Product[Category] = "Electronics")) Keeps existing filters and adds new ones. ALLSELECTED() CALCULATE(SUM(Sales[Amount]), ALLSELECTED(Region)) Respects user selections in visuals. ALLEXCEPT() CALCULATE(SUM(Sales[Amount]), ALLEXCEPT(Sales, Sales[Year])) Removes all filters except Year. Aggregation Summarize or aggregate data across rows or columns. Function Example Description SUM() SUM(Sales[Amount]) Adds all sales amounts. AVERAGE() AVERAGE(Sales[Amount]) Calculates mean value. COUNT() COUNT(Customer[ID]) Counts non-blank entries. COUNTROWS() COUNTROWS(Sales) Counts rows in a table. DISTINCTCOUNT() DISTINCTCOUNT(Customer[ID]) Counts unique customers. MIN() MIN(Sales[Amount]) Finds smallest sale. MAX() MAX(Sales[Amo

You know the story. You write a consumer, test it locally, deploy to staging, everything green. Then in production, the same message gets processed twice. Orders are duplicated. Notifications go out twice. Everyone blames "a race condition" and moves on. We spent the last few months formally verifying what actually happens when consumers crash with at-least-once delivery semantics. The short version: it doesn't matter which broker you use . The race condition is a property of the delivery contract, not a bug in the implementation. What we did Instead of writing more integration tests hoping to reproduce the timing, we wrote a formal specification of the consumer-broker interaction in TLA+ (the specification language by Leslie Lamport). The model checker exhaustively explores every possible interleaving of events — not just the ones you think to test. Then we took the counterexamples from the model and validated them in real running systems using Docker + Toxiproxy (network fault injection). If the math said "double-execution is possible", we confirmed it with actual containers. What we found We applied this pipeline to five different systems: Celery — ACK timeout + network blip: the model found the crash window at depth 9 (444 states). Chaos confirmed it: task executed twice. RabbitMQ — consumer stores result, drops connection before AMQP ACK. Same pattern. 108 states, depth 7. Chaos confirmed. NATS JetStream — consumer crashes after DB write, before ACK. 47 states, depth 6. Docker kill + Toxiproxy confirmed: duplicate execution. Apache Pulsar — batch consumer crashes between Process and SendAck. 21 states, depth 4. Chaos confirmed: 2 DB rows for 1 message. Kafka — consumer commits offset after processing. Crash between StoreResult and commitSync(). Same result: double execution. Every single time, the model predicted the collision and chaos confirmed it. The insight The spec is the same across all five systems. The variable names change, but the topology is identic

This is Part 1 of a 5-part series on Cisco EEM. We start here with the fundamentals and a few working applets, then build toward self-healing networks, automated diagnostics, compliance guardrails, and a complete real-world deployment. Ask ten network engineers what they use EEM for, and nine will say the same thing: "Oh, I have an applet that auto-recovers err-disabled ports." Then they never touch it again. That's a shame, because Embedded Event Manager is one of the most capable tools already sitting inside every Cisco IOS device you own — and almost nobody uses more than 1% of it. It's a full automation engine that lives on the box . No external server. No API gateway. No orchestration platform. Just the router or switch, watching itself, ready to react the instant something happens — even if the WAN is cut, the NMS is down, and it's three in the morning. This series is about using that other 99%. By the end you'll have a toolkit of applets you can deploy and, more importantly, a way of thinking about on-box automation. But we start at the foundation: what EEM actually is, how it's put together, and why "on the box" is a bigger deal than it sounds. What EEM actually is Embedded Event Manager is an event-driven automation framework built into Cisco IOS, IOS-XE, and NX-OS. Strip away the jargon and it's a very simple idea: When something happens, do something about it — automatically, on the device itself. That "something happens" is an event . That "do something" is one or more actions . Bundle an event with its actions and you have an applet — the basic unit of EEM. That's the whole model. The power comes from how many different things can be an event , and how much an action can do. Events EEM can watch for include: A syslog message matching a pattern (an interface flapping, an HSRP state change, a config being saved). An SNMP OID crossing a threshold (CPU over 85%, a power supply going absent, temperature rising). A CLI command being entered (someone typing wr

I wanted to build something different from the usual fan project. Instead of recreating gameplay, I asked a different question: How well do players actually remember the worlds of Super Mario Odyssey? The result is OdysseyGuessr, a browser game inspired by GeoGuessr. Players receive a screenshot from one of the game's Kingdoms and must place a marker on the correct location. Every meter counts. The project includes: Browser-based gameplay Single-player with customizable rounds Real-time multiplayer with a 5,000 HP battle system Community-submitted locations Admin moderation tools One of the biggest challenges wasn't the gameplay—it was collecting interesting locations that were difficult but still fair. Watching players confidently choose the wrong cliff or rooftop has been surprisingly entertaining. If you're interested in browser games or Nintendo fan projects, I'd love to hear your feedback. Play here: https://odyssey-guessr.lovable.app OdysseyGuessr is an unofficial fan project and is not affiliated with Nintendo.