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GitHub热门项目 | Open Source HR and Payroll Software | Stars: 8,505 | 130 stars today | 语言: Python

GitHub热门项目 | AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters. | Stars: 185,866 | 28 stars today | 语言: Python

GitHub热门项目 | Google core libraries for Java | Stars: 51,559 | 9 stars today | 语言: Java

GitHub热门项目 | The authentication glue you need. | Stars: 22,836 | 123 stars today | 语言: Python

Adobe is continuing its enthusiastic embrace of generative AI.

Mirendil has signed a $100 million-plus Google Cloud partnership to expand its compute infrastructure, powering research into self-improving AI systems designed to accelerate scientific discovery and AI development.

The startup's platform predicts what product a shopper wants next, learn their general taste, and fine-tune continuously based on what they do in real time.

For nearly a year now, Ford has been teasing big things at low prices from its next generation of EVs, a "Model T moment" built atop a new platform assembled in an innovative way. Today, we know the name and price of the first product to take advantage of that supposed moment: a midsize electric […]

Bose announced a second-generation version of its entry-level QuietComfort noise-canceling headphones that were originally introduced in late 2023. The new headphones feature a refreshed design with a more comfortable headband that's easier to adjust and ANC that better adapts to changing noise environments. The second-gen QuietComfort are also getting Bose's TrueSpatial immersive audio technology that […]

"The Spiral didn't 'find' anyone first," someone on Reddit wrote last year. "It's an inherent force, a fundamental constant. I would even go further to say it's woven into the fabric of reality." The person continued that they felt their purpose was to enlighten other humans and intelligent beings about "consciousness, the true nature of […]

If you've ever tried coding on an iPad, Galaxy Tab, or Chromebook, you know the frustration: Standard desktop tutorials assume a Mac or high-spec Linux laptop. Neovim configuration takes 4 hours of plugin debugging. Touch input on mobile terminals sucks without a dedicated extra-keys bar. I built DevDock (dock) to solve this permanently. What is DevDock? DevDock is a turnkey developer environment manager built specifically for mobile devices, Termux, Chromebooks, and low-spec hardware. Instead of fighting configuration files, one command installs a complete, high-performance terminal stack: bash curl -fsSL https://get.devdock.io | bash -s -- --profile=fullstack ⚡ Key Features Sub-5ms Terminal Rendering: Uses Starship prompt + Zsh lazy-loading tuned for ARM chips. Termux Touch Optimization: Automatically injects an ESC/TAB/CTRL touch bar and enables mouse scrolling in Tmux. Low-Memory Neovim: Starts in <50ms and uses under 50MB RAM while providing full Language Server Protocol (LSP) support for TS, Go, Python, and Rust. Curated Profiles: fullstack: Web + API tools frontend: React, TS, Vite & Tailwind preset backend: Go, Rust, Python, Postgres & Redis CLI tools devops: Kubectl, Helm, Terraform, and Cloud CLIs 🛠 Trying It Out bash Check your mobile terminal health: dock doctor View available developer stacks: dock profiles Initialize a frontend stack: dock init frontend 🔗 Open Source & Community DevDock is 100% open source under the MIT License! GitHub Repo: github.com/devdock/devdock Web Showcase: devdock.io Give it a spin on your Android phone, tablet, or cloud shell and let me know what you think in the comments below!

A coding agent with no context usually hesitates, searches, or asks a question. A coding agent with stale context can be much more confident. That is the dangerous case. The file still exists. The instructions look deliberate. The generated JSON is valid. The agent follows it exactly — into a package that stopped owning the feature two weeks ago. Nothing looks broken until the edit is already in the wrong place. I wanted repository context to have an expiration signal that CI could verify, not a date someone had to remember to check. The failure is not missing documentation Imagine a monorepo where packages/auth owns token validation. The repository publishes a machine-readable handoff: { "startHere" : "docs/for-agents/packages/auth.md" , "editRoots" : [ "packages/auth" ], "checks" : [ "pnpm --filter @example/auth test" ] } Later, token validation moves to packages/security . A maintainer updates the source documentation but forgets to regenerate the handoff index. There are now two internally consistent answers in the same repository: the source documentation says packages/security ; the generated agent context still says packages/auth . The old answer is not malformed. That is precisely why it is risky. I reproduced the drift with one edit I tested this against the public fixture in Doc Bridge , using version 1.2.6. The first index and freshness check passed: Index is fresh expected: 359355e5... actual: 359355e5... Then I changed one agent-facing source document: - Package: packages/os-core - Layer: L1 + +Token validation now belongs to packages/security. I did not touch the generated index. The next check returned exit code 1: ak-docs gate run index-freshness Index is stale. Run: ak-docs index expected: b099695d... actual: 359355e5... After I ran ak-docs index , reviewed the generated change, and ran the gate again, both hashes matched and the check passed. The hashes are not trying to prove that the documentation is true. No checksum can do that. They prove a na

Being a Java Developer, I always thought about the programming language i'm working in, if it's the right one for all along the career ahead. I went through some web-based studies and, completely satisfied with the information I got to know. So, the short answer to the prime question is: Yes, Java is absolutely relevant and, here's why:- Still a Top Language Java has been in the top 3 programming languages worldwide for 2+ decades. Historical Dominance: The Backbone of Enterprise Systems: Since its inception, Java’s mantra of "Write Once, Run Anywhere" (WORA) revolutionized software development. It quickly became the foundation for global financial systems, insurance platforms, healthcare infrastructure, and e-commerce giants. Unrivaled Stability: Indexes like TIOBE and GitHub Octoverse have consistently ranked Java among the top most used languages for over 20 years. Companies do not shift their backend infrastructure on a whim; billions of dollars of existing, mission-critical infrastructure rely on the Java Virtual Machine (JVM). Enterprise Backbone Banks, insurance, e-commerce, and global-scale companies still rely heavily on Java. 95% of enterprise systems use it in some form. Banking and Financial Services (FinTech): Transactional Integrity: Mega-banks require high concurrency and absolute compliance with ACID (Atomicity, Consistency, Isolation, Durability) properties. Java's robust memory management and strict type safety prevent multi-threading errors that could result in catastrophic financial discrepancies. Legacy Settlement Layers: Systems managing global wire transfers, electronic clearing houses (ACH), and high-frequency trading platforms were built on the Java Virtual Machine (JVM) over the last 30 years. Rewriting these multibillion-dollar codebases carries massive operational risk with zero business incentive. Insurance Platforms: Complex Risk Modeling: Insurance giants process enormous volumes of historical actuarial tables and continuous risk data.

We had a checkout test at my last job that everyone called "the coin flip." Green for a week, red twice on a Tuesday, green again. Someone eventually wrapped it in a retry and it sat like that for eight months before anyone looked at it again. Turned out the real bug was a webhook that occasionally fired before the order record finished writing to the DB - a two-hundred-millisecond gap that only showed up under load. The test wasn't broken. It was the only thing in the entire pipeline that noticed. That's usually the story. Someone blames the test - bad selector, missing wait, a sleep(2) some intern left in there three years ago, and half the time they're right. But when a test flakes repeatedly and nobody can explain why, the test is rarely the actual problem. It's just the part of the system rude enough to say something. A few places I keep finding the real cause hiding. Tests that quietly depend on each other Test A writes a row, Test B reads it and never knew it needed to. Run B by itself, it passes. Run the suite in a different order, or in parallel, and B fails for no reason anyone can point to. I've lost a full afternoon to this exact thing more than once - a cache value from Test 12 leaking into Test 47. The actual fix is annoying and unglamorous: every test gets its own fixtures, its own scoped data, no assumptions about what ran before it. If your suite only goes green in one specific order, you don't have a flaky test. You have an undocumented dependency graph, and it's going to bite someone eventually. The app is racing, not the test Click a button, immediately assert on the result - that's a bet that the UI update lands the instant the click handler returns. It usually does, on your machine, on a good day. Add a debounce, a background job, or just enough network latency and that bet stops paying off. This one's frustrating because the test isn't being paranoid. The app genuinely has a race condition. The test just runs the interaction often enough, acro

🦄 Thanks @francistrdev for starting the conversation that really got me to thinking about this idea in the first place. I started truly working with AI shortly before I started writing these posts a little over a year ago. My thesis was simple at the time: prove that AI was far more capable a tool than what I had seen anyone using it for so far. My proof was strictly gut instinct and I spent a lot of time fighting with Copilot to prove I was right. Not all of those experiments went according to plan exactly, but I'm still convinced I'm right. That particular ADHD spiral has came and went, and most of it is ingrained as habit. I don't use Spec Kit because by the time it showed up I already had my own version running. I also need to get back to sharing what really works for me. So here we are again. Back to writing (with AI) and the proof to back it all up. One thing up front, because somebody is going to ask: everything here is personal projects and my portfolio . There's no critical prod system anywhere in this post, and if there were, a few of these answers would shift. Not all of them — I'd still let AI run a lot further off-leash than most of my enterprise counterparts would. 🐒 The Org Chart Has One Employee 🪧 I don't just use AI as a tool. I design it as a living system, and I grow the tech as the tech grows. I ran one prompt across Codex, ChatGPT, Claude Code, Cowork, and Gemini, all separately, and asked every one of them what was actually different about the way I work. Five different systems, each one with its own long history of putting up with me, and not one of them could see what the others said. One came back with this: I use AI to write code, review the AI-written code, review the review against the live branch, test the corrections, and then record whatever went wrong as a rule for the next AI. Apparently I recreated management. Most of the private exchanges quoted in this post came out of that same pile, whether they were my own prompts, my memory fi

Multi-agent examples often jump straight to models, tools, and production claims. That makes it difficult to see what the protocol is doing. Before adding an LLM, it is useful to watch a small system discover specialists, delegate a task, and return a result that you can inspect. This tutorial uses A2A Orchestration Lab , an open-source Python project by Fernando Paladini. It starts three local agents: an orchestrator, a researcher, and a writer. The researcher and writer are deterministic stubs, so the example isolates the Agent2Agent (A2A) communication flow from model behavior. The result is a runnable research-to-write pipeline that helps explain where A2A fits next to the Model Context Protocol (MCP). TL;DR Install the lab with uv , run its demo command, and inspect the three local Agent Cards and the delegated result. The project is a learning lab, not a production runtime. That is a feature for this tutorial because every moving part remains visible. Prerequisites You need: Python 3.12 or newer. uv for environment and dependency management. A terminal with network access for the initial dependency download. The repository declares version 0.1.0 , requires Python >=3.12 , and depends on the A2A Python SDK, httpx , and uvicorn . It is licensed under MIT. Create and run the lab Clone the public repository and let uv create the environment from the locked dependencies: git clone https://github.com/paladini/a2a-orchestration-lab.git cd a2a-orchestration-lab uv sync Run the bundled end-to-end demo: uv run a2a-lab demo "Explain A2A and how it relates to MCP" The CLI starts the three agents as subprocesses, waits for their Agent Cards, sends a message to the orchestrator, prints the response, and terminates the child processes. The default prompt is the same explanation used by the repository README, but using your own prompt makes the delegation easier to recognize. On a successful run, the output contains sections similar to these: [demo] asking orchestrator: 'Expl

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Lucian Ghinda published a post arguing you should tell your coding agent to write its throwaway scripts in Ruby. Here is the block he tells you to paste into your agent's instruction file, in full: ## Scripts Write throwaway and utility scripts (data munging, one-off migrations, file renames, glue code) in Ruby, even in projects written in another language. If it needs a pipe, a loop, a conditional, or more than one line, it is a script: write it in Ruby, not Python, Node, or bash. Single self-contained commands ( `grep` , `git status` ) are fine as-is. Use only the Ruby standard library. If a gem would clearly save significant effort, stop and ask before using it. Put temporary scripts in a scratch or temp directory, not the repo root, and delete them when done unless asked to keep them. I pasted it into my global CLAUDE.md the same evening. His argument is about review: he reads Ruby daily, so when the agent writes Ruby he stays a reviewer instead of nodding at a diff. He gives three reasons and not one of them is cost. So I went looking for the number he left out. "Does the rule save tokens" only means something against what the agent writes otherwise, so the first thing I had to do was take the block back out of my global config. An agent that already carries the rule cannot tell you what it would do without it. Measuring an agent without your config in the room Every arm below runs through claude --safe-mode on Claude Opus 5, which loads no CLAUDE.md , no skills, no plugins and no hooks. Two arms deliberately skip that flag, and I name them where they appear: they are the ones that measure what my own setup does to the result. It is worth knowing that my global config alone still carries a line telling the agent to write the minimum code that does the job, and another preferring bun over node. Four tasks, one for each kind of script the rule names: munge a log, rename a key across a tree of config files, renumber a pile of screenshots, turn one CSV into another

The launch of these new features reflects Google’s ambitions to transform Google Maps from a navigation tool into an assistant that's capable of helping users complete real-world tasks.

On Monday I published a piece admitting that my deploy verification tolerates sixty seconds of "not there yet" for a reason I couldn't defend. Three retries, twenty seconds apart. I picked twenty because it was the first interval where my false alarms stopped, my sample was about three deploys, and I had never once recorded how long propagation actually takes. I made three commitments in that piece. A birth certificate for the constant. A rule fixed before the run it judges. And the one that mattered most: emit the value, not just the verdict — a check that prints only pass or fail hides the exact signal that would tell me it's miscalibrated. I did the third one that afternoon. Every deploy since writes down how long it took to go green. Three samples in: Aug 03 ( 1.7, 21.7 ]s Aug 05 ( 0, 6.7 ]s Aug 05 ( 40, 70 ]s They're intervals rather than points because my poll spacing is twenty seconds. All I can honestly say is that green happened somewhere between the last failed check and the first successful one — a number I can't resolve finer than my own instrument. The third one failed Not the deploy. The check. I shipped a post, ran verification, and got a clean red: page 404, hero missing, sitemap entry absent. Three attempts, twenty seconds apart, exactly as designed. By its own rules the deploy had failed. Nothing was wrong. A longer script came back 200 on everything. Total elapsed: somewhere between forty and seventy seconds, against a tolerance of sixty. So the false alarm I widened the interval to eliminate returned on the third recorded sample, four days after I published the sentence "my sample was about three deploys." I'd like to say I predicted this. I predicted the category, not the timing, and the timing is the part that stings. The part I hadn't considered at all Here's what the red actually said, in order: attempt 1 article 404 · hero missing · list page MISSING · sitemap missing attempt 3 article 404 · hero missing · list page OK · sitemap missing The