标签：#c

Fable 5 is back (for a week) but I just noticed that all the Claude models suddenly are failing with all the Tools ( search, fetch, read, write). Anyone else experienced the same?

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GitHub热门项目 | 🚀 A real-time monitoring dashboard for Claude Code, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, and an interactive web UI/MacOS/Windows native app. | Stars: 734 | 162 stars this week | 语言: TypeScript

This is a write-up on our company blog that I wrote, sharing our perspective into Hamiltonian Neural Networks (Greydanus et al., 2019) from a differential-geometry angle rather than the usual "here's the loss function" treatment. I've been working on HNN and LNN adjacent topics for years now and I found this particular lens made the *why* click in a way the standard framing never did for me, and I've been meaning to put everything in writing for a while now. I just feel like the Noether's Theorem which shows conservations can be mapped to symmetries (and in ML context, generalization) is not getting the attention that it deserves around physics informed neural networks. Also, it's a really beautiful architecture and I just love talking about it at every opportunity. It's math-heavy, but I did my best to sprinkle some tension relievers and interactive visuals here and there and make is as easy as it is to follow. Hopefully, I did a good job. I'd genuinely love to see your thoughts and your feedback submitted by /u/FlameOfIgnis [link] [留言]

Everyone keeps saying AI will let a solo developer take down the giants. And everyone keeps saying the giants will just absorb everything. Both takes are wrong , and I spent a while reading the actual 2025 data to figure out why. I pulled from four of the biggest developer datasets of the year: DORA 2025 State of AI-Assisted Software Development (Google Cloud, ~4,867 respondents) Stack Overflow 2025 Developer Survey (49,009 respondents) GitHub Octoverse 2025 (behavioral data across 180M+ developers) JetBrains State of the Developer Ecosystem 2025 (24,534 developers) Here's the honest synthesis. It's more useful than either hype narrative. The one-sentence thesis AI collapsed the cost of writing software to near zero. It did not collapse the cost of distribution, trust, support, or being liable when it breaks — and those are ~80% of what a software business actually is. So the effect isn't "solos beat giants." The effect is that the middle got hollowed out . The 10-person, VC-funded, me-too startup building a feature is the loser of this era — squeezed from below by a solo who ships the same thing for free, and from above by a giant who bundles it. Solos and giants both survive. The undifferentiated middle doesn't. "AI is an amplifier, not an equalizer" This is the single most important finding of 2025, and it comes straight from DORA: "AI's primary role in software development is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones." Read quickly, that kills the "AI levels the playing field" fantasy. AI rewards whoever already has good practices — not whoever is scrappiest. But read one layer deeper and it becomes the best available argument for the small team. DORA found the key enabler is independence of action — "the ability to develop, test, and deploy value independently, with little or no coordination cost." In an Adidas pilot they cite, teams in loosely-coupled architectures saw 20–30% produ

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A large-scale audit of AI-as-judge evaluation — covering over half a million individual judgments — finds that AI judges are consistently reliable but not valid, meaning they give the same answer repeatedly without that answer being correct. Published work and popular benchmarks like Chatbot Arena have treated consistency as proof of trustworthiness, and the audit shows that assumption is unfounded. Key facts What: Using one AI to grade another is now common — but the biggest audit yet shows these graders are consistent without being correct. A judge that always picks "answer A" scores perfectly on consistency. When: 2026-06-19 Primary source: read the source (arXiv 2606.19544) The distinction matters: a judge is reliable if it's consistent (same question, same answer), and valid if those answers are actually correct. The audit's central finding is that AI judges are reliable without being valid, and the field has been treating the first as evidence of the second. Because consistency is easy to measure and looks reassuring, it has stood in for actual trustworthiness across a lot of published work. A new audit makes the problem stark: a judge that ignores both answers and always picks the one labeled "A" would be perfectly consistent — flawless reliability, identical verdict every time — and completely worthless, because it never read anything. Consistency is trivially easy to fake and says almost nothing about whether the judging is sound. Yet "the judge agrees with itself" has done significant reassurance work in papers and benchmarks, and the always-pick-A example shows exactly how empty that reassurance is. When the researchers corrected for the agreement you'd get by chance — as any fair test should — confident-looking scores deflated noticeably. Gaps between models that seemed meaningful shrank or blurred. Accepted folk wisdom also took a hit: the long-standing worry that AI judges are suckers for longer, wordier answers turned out to be far weaker than assumed

Pressing enter to accept model suggestions now takes less effort than scrolling past it. One keystroke, and the code is yours. Reading it, understanding it, deciding if it's actually right, that part hasn't gotten any faster. That gap, between how fast we can accept code and how fast we can actually understand it, is where things start to go wrong. The new shape of technical debt We used to know where technical debt came from. Tight deadline, cut corner, # TODO: comment that nobody ever revisits. Rushing was the cause, and we could at least point to it. Now you can build up the same kind of debt on a calm Tuesday afternoon, no deadline in sight, just six suggestions in a row accepted because they looked fine and the flow felt good. Nobody rushed you, and the code still ended up just as unexamined. Same debt, just a different excuse. "It works" is not the same as "I understand why it works" Everyone knows that debugging is twice as hard as writing a program in the first place. So if you're as clever as you can be when you write it, how will you ever debug it? — Brian Kernighan, 1974 Fifty years later, the gap got wider. Kernighan was talking about code you wrote. At least you understood it once. A suggestion that compiles, passes the linter, survives code review and even comes with passing tests can still be standing on a wrong assumption that nobody caught, because nobody was reading it as code. They were reading it as output, and output that makes sense tends to get approved. Compiling is a low bar. Passing tests is a slightly higher one, depending on whether you wrote the tests, or its suggestion shaped or created those too. If it's the second, it's like grading its homework with its own answers. None of it tells you the logic is sound, that the edge cases are covered, or that it does what you actually needed, something we already learned every time we trusted code we didn't write. Somehow it's easy to forget it the moment the code appears inline, in our own edito

HTTP cookies are everywhere in authentication, sessions, and tracking — but reading Set-Cookie headers manually is tedious. I built a free, browser-only HTTP Cookie Inspector that parses cookie strings and gives you a security analysis. Live Tool 👉 https://devnestio.pages.dev/cookie-inspector/ What it does Parse Set-Cookie strings — extract all attributes at a glance Attribute cards — name, value, expires/max-age, domain, path, Secure, HttpOnly, SameSite Security score (0–100) — +25 for Secure, +25 for HttpOnly, +25 for SameSite≠None, +25 for expiry XSS/CSRF risk flags — warns when HttpOnly or SameSite is missing Syntax highlighted raw header — color-coded by attribute type Presets — session, persistent, secure+httponly, SameSite=Strict, minimal 100% client-side — no data leaves your browser Cookie security flags explained Flag Missing risk Present benefit Secure Cookie sent over HTTP Only sent over HTTPS HttpOnly JS can steal it (XSS) Inaccessible via document.cookie SameSite=Strict CSRF attacks possible Never sent on cross-site requests SameSite=Lax Partial CSRF risk Sent on top-level nav only SameSite=None Always cross-site Requires Secure flag SameSite values Set-Cookie: session=abc123; SameSite=Strict; HttpOnly; Secure # Best practice for auth cookies Set-Cookie: prefs=dark; SameSite=Lax # OK for non-sensitive preferences Set-Cookie: embed=true; SameSite=None; Secure # Cross-site embeds (e.g. payment widgets) Testing 84 tests, all passing ✅ Tests cover: Parsing all standard attributes Boolean flags (Secure, HttpOnly) detection SameSite value classification Max-Age duration calculation Security score computation XSS/CSRF warning logic All preset templates HTML escaping in output UI elements and copy functionality All tools at devnestio.pages.dev — free browser-only developer utilities.

Working With Massive JSON Responses Without Losing Performance Every developer eventually encounters it. You make an API request expecting a few hundred objects, and instead receive a response that's tens—or even hundreds—of megabytes. Suddenly your browser freezes, your editor becomes sluggish, and your application consumes gigabytes of memory. Large JSON responses aren't unusual anymore. Analytics platforms, cloud providers, search engines, AI services, ecommerce catalogs, IoT systems, and data export endpoints routinely generate enormous payloads. The good news is that handling massive JSON efficiently is mostly about choosing the right techniques. This guide covers the best practices that help you inspect, process, and optimize large JSON datasets without overwhelming your tools or your users. Understand Why Large JSON Is Expensive Before optimizing, it's helpful to know where the cost comes from. When an application receives JSON, it usually goes through several stages: Download the response. Store it as a string. Parse it into objects. Allocate memory for every property. Traverse the resulting object graph. For a 100 MB JSON file, peak memory usage can easily exceed 300 MB because both the raw string and the parsed objects coexist temporarily. This explains why applications often run out of memory long before reaching the actual file size. Don't Pretty-Print Gigantic Responses Immediately Pretty-printing is useful—but formatting a huge document all at once can consume significant CPU time and memory. Instead: inspect only the sections you need collapse large objects expand nodes on demand search before formatting If you need to examine a large payload in the browser, using a dedicated formatter designed for large documents can make navigation much easier. Tools like JSON Formatter allow you to validate, format, collapse, and inspect JSON without manually editing thousands of lines. Stream Instead of Loading Everything One of the biggest mistakes is reading an