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Un fondateur technique doit arrêter de coder le jour où son code freine plus son entreprise qu'il ne l'aide. Concrètement, trois signaux ne trompent pas : vous ralentissez votre propre équipe, vous ne managez plus, vous perdez la vue d'ensemble. Lâcher le code ne veut pas dire renoncer au produit ni à la technique : c'est passer de 90 % de code à 10 % de prototypage, pour récupérer le levier bien plus puissant d'un rôle de CPO, CTO stratégique ou CEO. Voici comment reconnaître le moment et organiser la transition. Vous avez fondé votre startup, écrit les premières lignes de code, recruté vos premiers développeurs. Et maintenant vous êtes toujours là, à relire chaque pull request, à refactorer du code le week-end, à être le seul à savoir déployer en prod. Vous savez que ce n'est plus tenable, mais vous n'arrivez pas à lâcher. Parce que lâcher le code, pour un fondateur technique, c'est renoncer à ce qui vous a défini depuis le premier jour. De fondateur à C-Level Cette transition est souvent l'occasion de clarifier le rôle que vous voulez jouer. Si le produit vous passionne, le rôle de CPO est une évolution naturelle. Si c'est la vision business, vous devenez CEO. Ni l'un ni l'autre ne nécessite de coder 8 heures par jour. Les trois signaux qu'il est temps d'arrêter Avant de les détailler, voici les trois signaux en un coup d'œil, avec ce qu'on observe sur le terrain et le risque sous-jacent. Si vous vous reconnaissez dans ne serait-ce qu'un seul, il est temps d'agir. Signal Ce qu'on observe Le risque pour l'entreprise Vous ralentissez l'équipe Tout passe par votre validation, vous réécrivez le code Goulot d'étranglement, équipe qui n'ose plus proposer Vous ne managez plus Recrutement, 1-1, vision à 12 mois passent à la trappe Démotivation et départs silencieux des seniors Vous perdez la vue d'ensemble Vous confondez intéressant techniquement et utile au client Mauvaises décisions stratégiques, dérive produit Vous ralentissez votre propre équipe C'est le signal le pl

3 platforms, one queue, zero editorial control. That was the state of my content automation before I sat down to spec the dashboard. LinkedIn, X, and Threads each had their own generator, their own state files, their own publishing loop. Drafts got generated, passed a quality gate, and fired into the void. If the draft was mediocre or the timing was wrong, I found out after the fact. The problem is not the automation. Automation is why I can run three platform engines without spending two hours a day managing content. The problem is that zero editorial visibility means you cannot catch the bad ones before they post. What I wanted: see every draft before it goes out. Edit inline if needed. Post immediately or schedule for the next slot. Compose something manually when I have a specific take to push. Keep the comment automation untouched because that runs high frequency, low stakes, and babysitting individual replies defeats the point. The spec came out to three core flows. Review queue. Every pregenerated draft surfaces here with full context: platform, topic, generation timestamp, quality score. One click to edit inline, one to approve for the next slot, one to post immediately. The goal is a 30 second review per draft, not a full editing session. Manual compose. Sometimes I know exactly what I want to say. A text area, platform selector, and post button. No generation, no queue, just publish. This is the escape hatch for when something is happening in real time and the pregenerated queue is irrelevant. Schedule view. A simple calendar showing what is queued for which slot across all three platforms. The generator already handles slot logic and quiet hours. The dashboard just needs to surface the state so I can see gaps and move things around without touching JSON files directly. What I deliberately left out: comment automation. That pipeline runs separately, fires frequently, and does not benefit from human review on every reply. Adding it to the dashboard would cr

"Privacy by design" is one of those phrases you read everywhere and rarely understand. It is often treated as a document to attach to a project, a box to tick before going live. In reality it is not a piece of paperwork: it is the way software is conceived and built from the very first line, so that it protects people's data without anyone having to remember to do so afterwards. What the GDPR actually says The principle is written plainly in Article 25 of the GDPR, which speaks of "data protection by design and by default". These are two distinct things. Protection by design concerns the choices made while the system is being built. Protection by default concerns how the system behaves the moment it is switched on, before anyone touches a single setting. The law does not mandate a specific technology. It asks for an outcome: that data protection be built into the system, proportionate to the risks, and not bolted on afterwards as a patch. It is a difference of substance, not of form. A well-designed system does not have to chase compliance: it already has it inside. It is not a document, it is an architecture The most common mistake is to reduce privacy by design to a file. A report is written, filed, and the building goes on exactly as before. But a PDF protects no data. What protects data are the technical decisions: what information is collected, where it is stored, who can see it, how long it stays, what happens when it is no longer needed. These decisions are made at design time, and changing them later costs far more than getting them right at the start. The principles, turned into concrete choices Privacy by design becomes useful only when it stops being a slogan and turns into a series of choices. Translated into practice, the principles sound like this. Minimisation. You collect only the data genuinely needed to deliver the service. A field you do not collect does not need protecting, cannot be lost in a breach, does not need keeping. The safest piece of da

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…

If you've ever done a timed C++ coding assessment on a platform like HackerRank or DevSkiller, you know the friction isn't really the algorithm — it's the loop . Download a zip with a weird filename, unzip it, hunt for the project root, configure CMake, build, run GTest, fix one failing test, repeat... and somewhere in there you've burned ten minutes of your one-hour window just fighting the harness instead of writing code. These platforms' in-browser editors are fine for quick problems, but for anything involving multiple files (headers, sources, a real test suite), I'd rather work in my own terminal and editor. The catch is that you still have to get the project out of the browser sandbox, build it locally with the exact same toolchain (CMake + GTest), and then package it back up in a way the grader will accept. So I wrote three small bash scripts to remove that friction entirely. Sharing them here in case they save someone else the same ten minutes. The workflow Download the project archive from the platform (zip or tar.gz, filename is whatever the platform gives you — often randomized) Extract it — script 1 handles this regardless of filename or archive type Iterate — script 2 configures CMake once, then repeatedly builds and runs GTest, optionally watching for file changes Package — script 3 strips build artifacts and any local helper scripts, then zips it back up under a name that won't collide with the original download, ready to re-upload Script 1: extract_and_setup.sh Most of these platforms hand you an archive with an unpredictable filename. This script extracts whatever you point it at ( .tar , .tgz , .tar.gz , or .zip ), figures out which directory it unpacked to by diffing the folder listing before and after, and drops the build script into it automatically. #!/usr/bin/env bash # extract_and_setup.sh # Extracts $fname (tar, tgz, tar.gz, or zip) into the CURRENT folder, # then copies run_build.sh into the directory that was created. # # Usage: # ./extrac

If you have ever opened a system design article, seen a diagram with twelve boxes, three databases, a message queue, and the words "eventually consistent," and quietly closed the tab, this post is for you. There is a myth that you need years of experience running large systems before you can learn system design. You don't. Plenty of engineers learn it before they have ever deployed anything bigger than a side project. What you actually need is the right starting point and a way to build intuition without access to production-scale traffic. That is exactly what this guide gives you. "But I've never built anything at scale" Good news: neither had most people the first time they learned this. System design is not a memory test about how Uber works. It is a thinking skill: given a vague problem and some constraints, make a sequence of reasonable trade-offs and explain them clearly. That skill does not require having operated a system serving millions of users. It requires understanding what the moving parts do and practicing the reasoning. The experience helps later, but it is not the price of entry. So drop the idea that you are "not ready." You are ready to start today. The honest minimum prerequisites You do not need much, but you do need these four things. If any feels shaky, spend a few days here first; it will save you weeks of confusion later. What happens when you load a web page. Client sends a request, DNS resolves a name to an address, a server responds. If you can sketch that, you're fine. The two kinds of databases. Relational (tables, rows, SQL) versus non-relational (documents, key-value). You don't need to be an expert, just know they exist and roughly when each fits. What an index is. A way to find data fast without scanning everything. That one sentence is enough to begin. Basic estimation. If something gets a million requests a day, roughly how many is that per second? (About 12, for the record.) The ability to do rough math out loud matters more than

Overview AGENTS.md is useful. It gives AI coding agents a place to find repo-specific guidance: how to behave what conventions matter what areas need extra caution what kinds of changes should trigger review That is a meaningful improvement over sending an agent into a repo with no instructions at all. But AGENTS.md is not enough. It can tell an agent to be careful. It cannot, by itself, make execution safe, verification trustworthy, or review inspectable. For that, a repository needs more than instructions. It needs: declared safe commands a canonical verification path receipts that show what actually ran That is the difference between agent guidance and execution governance. Instructions Help. They Do Not Govern Execution. An instruction file is still prose. That means it can express intent, but it does not automatically create operational truth. For example, AGENTS.md can say: run the right checks before handoff avoid destructive commands do not edit generated files ask before touching infrastructure Those are good rules. But notice what they leave unresolved: which checks are the right ones which commands are actually safe which paths are protected structurally versus only suggested what should count as evidence that verification happened how to tell whether a failure came from code, setup, or drift That is where many agent workflows still break down. The agent may follow the spirit of the instructions and still take the wrong execution path. Safe Commands Need To Be Explicit One of the biggest gaps in agent-oriented repos is that they often declare guidance without declaring a safe command surface. The repo may tell the agent: Run tests before you finish. But that still leaves a dangerous amount of interpretation. Which task is safe? Is it: npm test pnpm test make check docker compose run test a narrower unit-test path the CI workflow itself And if several exist, which one is canonical for a routine code change? The repo should not force the agent to infer that

The Federal Trade Commission fined Amazon $2.25 million to settle claims that the company failed to help customers who fell victim to identity theft, as reported earlier by Bloomberg. In its complaint, the FTC accuses Amazon of refusing to provide customers with information about purchases made with fraudulent accounts, in violation of the Fair Credit […]

The California-based startup enters a crowded headphone market with a $399 chiseled-aluminum gamble.

"That would be an awesome capability."

EquiLibre Technologies, a Prague-based AI lab founded by three ex-DeepMind researchers, is now valued at more than $500 million.

Supreme Court will weigh if Apple contempt finding in Epic case is “erroneous.”

Apple filed an appeal after being found in contempt of an order related to App Store fees.

As high memory and storage prices have driven up the cost of everything from consoles to computers, finding a competent laptop for under $1,000 has become a challenge. Thankfully, the Acer Swift Go 16 AI is on sale for $899.99 at Best Buy, a steep discount from its usual list price of $1,549.99. The well-equipped […]

Google's NotebookLM is adding a new way to catch up on your notes: TikTok-style AI videos. The new feature is rolling out to Google AI Ultra and Pro subscribers, allowing NotebookLM to generate 60-second vertical AI clips based on the sources you upload to the app. The example shared by Google details Australia's unsuccessful war […]

“We can take power from a plasma,” Kieran Furlong, co-founder and CEO of Realta Fusion, told TechCrunch. The milestone shows “what’s possible,” he added.

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GitHub热门项目 | A comprehensive dataset of 433 fitness exercises. Each entry includes name, category, target muscle group, equipment, instructions, thumbnail image, and animation video. | Stars: 6,139 | 1,413 stars today | 语言: HTML

Security Team: “I have a major Grype...with what I Syfted out of your provided image." Developer: “Well your Grype is slowing me down...let’s tone it down a notch.” While deploying bookstack into my local environment, this issue surfaced. It is true for many organizations today deploying images and packages in their environment. How can this noise fatigue in the software supply chain be remedied? Add a .gype.yaml file to the root directory of your project. This will allow grype to ignore certain CVE's that do not execute or pose a threat in your environment. The yaml config can be as simple as below: Linux Environment # grype.yaml ignore : - vulnerability : CVE-2026-32631 reason : " Platform-specific false positive. Git for Windows only; not applicable to this Linux-based image." - vulnerability : CVE-2016-2781 reason : " Chroot escape via ioctl. Containers rely on namespaces/cgroups, not chroot, so this path isn't exploitable here." OR # grype.yaml ignore : - vulnerability : CVE-2026-32631 - vulnerability : CVE-2016-2781 This will help developers and security engineers get along better. 😃 Grype config reference: https://oss.anchore.com/docs/reference/grype/configuration/

After several weeks of optimization and community feedback, AnimaStage Lite v1.2.3 is now available. The biggest milestone of this release is that AnimaStage Lite is now available on Google Play, alongside the browser version. 📱 Google Play https://play.google.com/store/apps/details?id=com.webmmd.suite 🌐 Browser https://animastage-lite.app What's new in v1.2.3 📱 Google Play Release AnimaStage Lite is now officially available on Android through Google Play, making it easier to access the editor without manually installing APKs. ⚡ Multi-model performance improvements Working with multiple characters is now much smoother. Improvements include: Performance governor now reacts to the number of visible models. Background characters use a lighter rendering path. When playback is paused, Bullet Physics is simulated only for the selected character. Bullet Physics substeps are capped to improve stability and maintain FPS. 🔄 Physics stability A new Global Physics Stability Registry helps keep simulations more reliable across different scenes. Added: Fix Physics — a soft physics reset that restores the simulation without interrupting the animation timeline. This was implemented after feedback from users who experienced unstable physics when working with multiple models. 🛠 Bug fixes Fixed: SITE_URL is not defined in officialProject.ts General stability improvements Various internal cleanups Project goals AnimaStage Lite is an experimental browser-native MikuMikuDance studio built with WebGL and WASM. Current features include: PMX / PMD support VMD animation playback Bullet Physics Timeline editor MP4 export Browser + Android support The long-term goal is to make MMD creation accessible without requiring a desktop installation. Links 🌐 Website https://animastage-lite.app 📱 Google Play https://play.google.com/store/apps/details?id=com.webmmd.suite 💻 GitHub https://github.com/FBNonaMe/animastage-lite Feedback, bug reports, and feature suggestions are always appreciated. Every relea