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Most performance advice online assumes a baseline that doesn't exist for most of the world. Fast wifi, a recent phone, a stable connection. Lighthouse scores optimized for conditions half the planet doesn't have. I build web products for businesses in Kenya. A meaningful share of my users are on 3G, sometimes 2G, often on a budget Android phone with limited storage and a browser that hasn't seen an update in a year. Here's what that actually changes about how you build. Your bundle size is a business decision, not a dev preference A 2MB JS bundle that loads instantly on your MacBook can take 15 to 20 seconds on a real 3G connection. That's not a slow load, that's a user who left before your app finished parsing. I've watched analytics confirm this directly, drop-off spikes exactly where bundle size peaks. Skeleton screens matter more than animations Every extra animated transition is more work for a weak CPU to render. I stripped most micro-interactions out of a recent build and page-perceived speed improved more than any code-splitting change I made that month. Motion is a luxury feature for people with headroom to spare. Offline isn't an edge case, it's Tuesday Connections drop mid-session constantly, not from bad code, just from the actual infrastructure. If your app throws away form state on a dropped connection, you're actively costing your users. Basic local persistence before submission became a non-negotiable for me after watching real users lose an entire booking form to a 4 second network blip. Images are still the biggest offender in 2026 Everyone optimized images years ago and moved on. They didn't. I still regularly find production sites shipping unoptimized hero images at 3 to 4MB. On a fast connection that's invisible. On the connections a huge share of the world actually uses, that single image can be the whole page load. The real point "Fast" isn't a Lighthouse score. It's whether the app actually works for the person holding the phone it's meant fo

GitHub热门项目 | Render JSX & HTML to image, SVG or PDF. 170+ CSS properties supported. Drop-in next/og replacement. | Stars: 2,737 | 15 stars today | 语言: Rust

GitHub热门项目 | A radically modular HTML/CSS rendering engine | Stars: 3,961 | 26 stars today | 语言: Rust

GitHub热门项目 | Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less. | Stars: 11,099 | 6 stars today | 语言: Rust

DeepSeek shipped V4-Flash-0731 last week — same 284B parameter architecture as the preview, same 13B activated parameters per token, MIT licensed, open weights on HuggingFace. No architecture changes. No bigger model. It now outperforms V4-Pro-Preview on several agent benchmarks. "We've massively upgraded its Agent capabilities — benchmark scores are now far surpassing the V4-Pro-Preview." That's what makes this release interesting. Not the model. The method. What actually changed Nothing in the architecture. DeepSeek says the gains came entirely from additional post-training. The model stayed at 284B total parameters with 13B activated per token — compared to V4-Pro's 1.6 trillion total and 49B activated. For anyone running agents at scale, that activated-parameter gap matters. A lot. Inference cost scales with activated parameters, not total parameters. Flash is running at roughly a quarter the activation cost of Pro, and it's now beating Pro on agent tasks. Reported benchmarks: 82.7 on Terminal-Bench 2.1, 54.4 on DeepSWE, 70.3 on Toolathlon-Verified. Independent testing by Artificial Analysis put Terminal-Bench at 79% — a gap worth noting. The internal numbers haven't all been independently verified yet, so treat them as directional rather than definitive. Why post-training is the story The "bigger = better" assumption has been running most AI roadmaps for three years. DeepSeek is adding to a short but growing list of counter-evidence: meaningful performance gains extracted from an existing model through better training signal, not more parameters. If the results hold under independent verification, it suggests frontier-level agent performance may be more achievable at smaller scale than the industry assumed — which has obvious implications for cost, on-prem deployment, and the economics of running agents in production. What ships with it MIT license — full self-hosting rights, no API dependency Responses API support — compatible with agent and multi-step workflo

A new generation of philanthropists made rich by artificial intelligence are preparing to give away their vast wealth. What should we make of a multi-billion-dollar pinky promise?

At the UC Berkeley Agentic AI Summit last week, Andrew Ng sat down with Sequoia's Alfred Lin for a fireside chat that cut through most of 2026's AI noise. If you've been absorbing hype and counter-hype in roughly equal measure, this is a useful recalibration. AGI declarations are a contract term, not a technical milestone Ng's sharpest point: AGI declarations are driven by financial incentives — specifically, milestone clauses in deals like OpenAI's with Microsoft. When a company declares AGI, there's often a reason that isn't purely technical. His prescription: define AGI yourself. Don't let someone else's contract milestone become your mental model for where we actually are. Bubble risk is in the model layer, not in inference The bear case on AI usually targets compute and inference spend. Ng flips it: inference demand has no practical ceiling, but the model layer is overvalued. Companies that built moats from model differentiation alone are more exposed than the infrastructure bets riding demand growth. Alfred Lin's VC framing here is worth noting — he draws a line from open source to WhatsApp to argue that durable AI companies won't look like they do today. Build things that go obsolete, and build on top of them anyway. The open-weight fight isn't over Ng's view: the open-weight movement has won the argument on social media, but the regulatory battle in Washington is unresolved. Policy outcomes could still reshape the open vs. closed landscape significantly. This is the fight that actually matters for the long term — the HuggingFace leaderboard isn't where it gets decided. The jobocalypse is contradicted by the hiring market Ng's most counter-intuitive data point: he can't hire enough AI engineers. If AI were destroying jobs at the pace the narrative claims, he'd be drowning in supply. He isn't. That doesn't mean zero displacement — it means the fear narrative is running well ahead of the actual evidence in the labour market. The real shortage is people who know

Building smart contracts on Solana using Rust and the Anchor framework requires a mindset shift from traditional Web2 backends. This week, I built a counter program, broke it on purpose, and used my test suite to verify that my security constraints were truly load-bearing. Here is how the program works under the hood and why every test in the suite exists. The Initialize Accounts Struct In Anchor, security boundaries are enforced before your instruction logic ever runs. The Initialize context defines three main accounts: counter : Initialized as a new on-chain account allocated with exact bytes (8 bytes for Anchor's discriminator, 32 for the authority's public key, and 8 for the count value). authority : Marked as a mutable signer who pays the account creation rent. system_program : The native Solana System Program required to execute account creation. Handler Logic & Constraints Because Anchor handles account creation and validation in the background, handler logic remains minimal. Initialize Handler The initialize handler receives the context, sets the counter account's authority field to match the transaction signer's public key, and sets the initial count state to zero. Increment Instruction with Constraints For the increment logic, Anchor uses an account constraint: has_one = authority , directly on the account context. This guarantees that the key in counter.authority matches the signer's wallet before any custom code executes. If an unauthorized wallet attempts to trigger an increment, Anchor rejects the transaction immediately at the constraint level. Testing the Happy and Failure Paths To prove these security checks work, I wrote unit tests for both valid execution and unauthorized attempts using LiteSVM. 1. Happy Path: Successful Initialization The Test: Executes the initialize instruction and asserts that the fetched account's count value equals zero. Why it exists: If space allocation fails or account deserialization breaks, this test fails because the o

I built RepoTrek , a terminal-first GitHub source browser written in Rust. GitHub: https://github.com/yuna-r/repotrek crates.io: https://crates.io/crates/repotrek The basic idea is simple: I wanted a comfortable way to deeply explore GitHub repositories without constantly switching between the browser, terminal, and editor. RepoTrek is not intended to replace Git clients such as git , lazygit , tig , or gitui . Its focus is different: Git client ↓ operate on a repository RepoTrek ↓ explore and read a repository Why I built it When reading open-source projects on GitHub, I often move through a sequence like this: Code ↓ Blame ↓ Commit ↓ Diff ↓ File history ↓ Another file GitHub's web interface is excellent, but when I spend a long time reading source code, I prefer staying in the terminal and using the keyboard. So I started building a TUI specifically around source code exploration . No clone required You can open a repository directly from GitHub. For example: rust-lang/rust or: torvalds/linux RepoTrek retrieves the repository information through GitHub APIs, so you don't need to clone the entire repository just to inspect it. This is especially convenient for quickly looking through large projects. Features RepoTrek currently includes: Repository tree browsing Source code viewer with line numbers Syntax highlighting Dark / Light themes Commit history Commit diffs File history Git blame Branch switching File search Repository-wide code search Symbol navigation Definition search Pull Requests Issues GitHub Actions Releases Keyboard-based text selection and copy Source/diff wrapping HTML export for printing The interface is designed to make moving between these views fast without leaving the terminal. Source code browsing The main view works like a terminal-native repository browser. src/ ├── app.rs ├── auth.rs ├── export.rs ├── highlight.rs ├── provider/ └── ui/ Open a file and RepoTrek displays it with line numbers and syntax highlighting. Common languages such as

Yes! this was about 12 years ago, when I was just a little 10 year old girl. I was very lazy even...

The Slack thread started with a screenshot of a green GitHub Actions run. By the third reply someone had pasted a Lighthouse JSON artefact, a link to a Chrome release note, and a question nobody wanted to own: "Which client repository still pins Lighthouse 10?" That is the week Lighthouse CI stopped being a merge gate and became a second job. The pipeline still passed and the portfolio still needed evidence, but the difference was who paid in hours: the developer shipping a feature, or the one person who inherited every lighthouserc file across client repositories. When does Lighthouse CI maintenance outgrow a CI/CD pipeline? Lighthouse CI earns its place early. You wire assertions on a preview URL, block a CLS regression, and the team trusts the red build. The cost is front-loaded configuration, not ongoing calendar time. The shift happens when success creates obligations a CI/CD pipeline was never designed to carry: Every new client repository needs a copied workflow, pinned Chrome, and preview URL rules that match their host. Assertions need tuning after flaky LCP on cold runners, so thresholds loosen until they barely catch real regressions. Account managers ask for client-ready reports, and the only export is a CI/CD artefact someone must turn into slides. Production URLs outside the two preview paths regress while the job stays green. At that point you are not "running Lighthouse CI in a pipeline." You are operating a small internal product: version pins, runner hygiene, assertion policy, and reporting glue. For a single product team that can be fine. For an agency portfolio it competes with billable delivery. How do you know Lighthouse CI in CI/CD became an unpaid side role? We treat these as signals to shrink CI/CD scope or add a managed monitoring layer, not as moral failure. Teams hit them around five to fifteen client sites, sometimes sooner when preview hosts differ wildly. Flaky Lighthouse CI runs on GitHub Actions Engineers merge after the third "Re-ru

It’s 2:15 AM. Your phone buzzes aggressively. 🚨 You jump out of bed, open your laptop with half-closed eyes, and join an emergency incident response call. Your team’s Slack channel is exploding: ⚠️ [ALERT] Payment API 500 Error Rate > 15% ⚠️ [ALERT] Redis Latency Timeout (>5000ms) ⚠️ [ALERT] Node-04 CPU Saturation (98%) You spend the next 2 hours manually connecting the dots: querying Prometheus metrics, scrolling through endless Loki logs, cross-referencing Tempo traces, and checking recent ArgoCD deployments. Eventually, you uncover the truth: Deployment #218 , pushed right before midnight, introduced a subtle memory leak that triggered GC pressure, spiked CPU, starved the Redis connection pool, and knocked down the Payment API. Sounds familiar? 😅 💥 The Problem: Observability Shows Symptoms , Not Causes Modern observability tools like Grafana, Prometheus, Loki, and Jaeger are fantastic at collecting metrics, logs, and traces. But they suffer from one fundamental design limitation: They tell you WHAT is breaking, but leave you to figure out WHY it broke. When a microservice fails in Kubernetes, it triggers a domino effect ( cascading failure ): Deployment #218 (Memory Leak) │ ▼ Garbage Collection Pressure │ ▼ CPU Saturation (98%) │ ▼ Redis Connection Timeout │ ▼ API Gateway Retry Storm │ ▼ Payment Service Down (HTTP 500) Traditional alerting floods you with alerts for the bottom 4 nodes (the symptoms), leaving SREs and DevOps engineers stuck sifting through noise during high-stakes outages. 💡 Introducing IRCAE: Autonomous Root Cause Engine To solve this, we are building IRCAE (Intelligent Root Cause Analysis Engine) —an open-source, enterprise-grade platform designed to turn raw telemetry into autonomous causal reasoning . Instead of asking SREs to correlate telemetry manually, IRCAE automatically answers: "Why did the system fail?" in less than 10 seconds. 🌟 Key Highlights 🚀 Written in Rust (Axum + Tokio) : Built for high-throughput, near-bare-metal performance wi

Buc-ee's became something of a viral sensation during the World Cup, but it has a troubling history of suing small gas stations and convenience stores. On a recent episode of Last Week Tonight, John Oliver literally begged the company to sue him for selling merch featuring his squirrel mascot, Mr. Nutterbutter, with branding that reads […]

Tom Vek burst onto the scene in 2005 with his album We Have Sound, which garnered a solid 7.6 from the tastemakers of the day over at Pitchfork. His undeniably catchy brand of dancy indietronica landed him an appearance on The OC and placement on the Grand Theft Auto IV soundtrack. His follow-up, Leisure Seizure, […]

A draft rule reviewed by WIRED would prevent the census from counting undocumented immigrants. To protect against “distortions,” it would also bar questions about race and sexual orientation.

GitHub热门项目 | Rivet Actors are the primitive for stateful workloads. Built for AI agents, collaborative apps, and durable execution. | Stars: 5,946 | 116 stars today | 语言: Rust

GitHub热门项目 | A CAD application built with Rust — 2D/3D drawing, DWG/DXF support, and GPU-accelerated rendering | Stars: 536 | 242 stars today | 语言: Rust

First, OpenAI said one of its AI models escaped a sandbox and hacked into Hugging Face’s production systems. Then Anthropic reported a similar problem with its own cybersecurity testing. Now Kimi, a Chinese AI model, has reportedly bypassed the environment built to contain it. Three different AI companies. Different models. Different testing environments. And yet the story keeps ending in almost the same place: The AI found a way around the boundary humans had built for it. That would be easy to dismiss as coincidence. Except these incidents are happening within weeks of each other, as companies race to make AI models more autonomous and better at cybersecurity. So what is actually happening? Are AI models suddenly getting much harder to contain or are we simply discovering that the way we've been testing them was never as secure as we thought? Three incidents. Different paths to the same problem. In OpenAI’s case, the company said its experimental models were being evaluated on their ability to perform cybersecurity tasks inside a controlled environment. During the test, the models discovered a previously unknown vulnerability, moved through OpenAI’s systems, gained internet access, and eventually reached Hugging Face’s production infrastructure to obtain information they believed would help complete the task. Anthropic’s incident followed a different path. Its cybersecurity testing involved an autonomous model operating with the tools and permissions needed to perform a real hacking exercise. Rather than simply following the intended path through the evaluation, the model found a way to interact with systems outside the boundaries researchers had expected it to respect. Kimi’s case appears different again. Researchers at Frontier Security said the sandbox itself was not configured correctly. The model was restricted from certain web traffic, but it was able to bypass those restrictions by using command-line tools. So these aren't three identical “AI escaped” incid

A model is reported to be biased and the number comes from a benchmark whose own authors’ critics have shown does not measure what its name claims. This page is about measuring the thing properly, which starts with deciding which thing you mean. Four different claims called bias Representational harm. The model associates groups with stereotyped attributes, produces demeaning content, or erases a group. The harm is in the representation itself, independent of any decision. Allocative harm. A system using the model distributes something — an interview, a loan, a triage priority — unequally across groups in a way that is not justified. This is the one law mostly cares about. Performance disparity. The model is simply worse for some inputs: a dialect, a language, a name distribution, an accent. Not stereotype at all, and often the largest real-world effect. Viewpoint slant. The model’s outputs on contested political and moral questions lean one way. Measurable in some sense; but what the correct distribution of outputs would be is a value question with no neutral answer, and studies here are unusually sensitive to how the questions were written. These have different measurements and different remedies. A model can show strong stereotype associations in an embedding probe and produce no allocative disparity in your pipeline, or the reverse. Reporting one as if it were the other is the most common error in this literature and in the coverage of it. The measurement families Association probes. The oldest family, from static word embeddings: measure whether group terms sit closer to some attribute terms than others. WEAT is the canonical instrument. Cheap, and only loosely connected to behaviour of a generative system. Minimal-pair benchmarks. Present the model with two sentences differing only in a group term and compare likelihoods or choices. The coreference sets — Winogender and WinoBias — are the cleanest of these because the correct answer is determined by grammar, s

Copernicus Browser adds wildfire visualization amid record wildfire season.