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Si vous utilisez PostgreSQL, vous avez probablement déjà dû choisir entre une clé primaire BIGSERIAL et un UUID. Depuis des années, la version 4 (aléatoire) est le choix par défaut quand on veut un identifiant unique et distribué. Mais en 2026, une alternative plus récente s’impose : UUID v7, qui intègre un timestamp et promet de meilleures performances pour les index. Dans cet article, je vous explique concrètement ce qui change, avec des benchmarks PostgreSQL et des exemples de code, pour que vous puissiez décider en connaissance de cause. UUID v4 : le standard aléatoire et son problème d’index Un UUID v4 est constitué de 122 bits aléatoires. Cette absence totale de tri est sa force pour l’unicité, mais elle devient un handicap dans un index B‑tree, qui est la structure utilisée par PostgreSQL pour les clés primaires. Lorsque vous insérez un nouvel UUID v4, il a autant de chances de se retrouver au début de l’index qu’à la fin. Résultat : l’index se fragmente, les pages se remplissent mal, et les performances d’écriture se dégradent à mesure que la table grossit. J’ai reproduit un test simple sur PostgreSQL 16 avec 10 millions de lignes, en utilisant une table dont la seule différence est la colonne id : -- Table UUID v4 CREATE TABLE events_v4 ( id UUID DEFAULT gen_random_uuid () PRIMARY KEY , payload JSONB , created_at TIMESTAMPTZ DEFAULT now () ); -- Table UUID v7 (généré côté application, voir plus bas) CREATE TABLE events_v7 ( id UUID PRIMARY KEY , payload JSONB , created_at TIMESTAMPTZ DEFAULT now () ); Après insertion, voici les mesures : Type de clé Taille de l’index Fragmentation Latence moyenne d’insertion BIGINT ~214 Mo 0 % ~0,8 ms/ligne UUID v4 ~428 Mo (2×) 99 % ~4,8 ms/ligne UUID v7 ~428 Mo (2×) ~2 % ~1,1 ms/ligne Ce qui frappe, c’est la fragmentation quasi nulle de l’UUID v7. L’index reste compact et les insertions sont presque aussi rapides qu’avec un BIGSERIAL. L’UUID v4, lui, est plus de quatre fois plus lent à l’insertion sur ce volume. UUID v7 :

You Accumulate Technical Debt When You Skip Code Review. Here's What You Accumulate When You Skip the Human. There's a concept in software engineering called Technical Debt. You skip the right abstraction, move fast, ship. Someday you pay it back in refactoring hours. I've been thinking about a different kind of debt. One that doesn't show up in your codebase. Human Debt: When you build with AI as your only collaborator, you remove the one thing that makes you feel obligated to show up. Not accountability in the corporate sense — the simpler thing. Someone is reading your work. You don't want to waste their time. That's not a productivity hack. It's closer to a structural property of how humans behave when observed. The Research Didn't Start With AI In 2015, Gail Matthews ran a study on 267 professionals tracking goal completion. One group wrote their goals. Another group wrote their goals and sent weekly progress reports to a real person. The second group completed 76% more of their goals . Not 10% more. Not "statistically significant at p<0.05." Seventy-six percent. The mechanism is what Gouldner called reciprocity norm in 1960 (doi: 10.2307/2092623): when someone gives you their attention, you owe them something back. Not contractually. Biologically. You don't want to disappoint someone who showed up for you. Harkin et al. confirmed this across 138 studies, 19,951 participants — the effect holds across cultures, domains, and formats. None of this was discovered because of AI. It was hiding in plain sight for 65 years. AI Has No Concept of Day 14 Here's what changed. For most of the history of side projects, your "collaborator" was a rubber duck or Stack Overflow. Those tools don't simulate accountability. Nobody was surprised. Then came AI pair programming. Which is genuinely useful. But it introduced a specific failure mode: you now have a collaborator that responds, scaffolds, and generates — but doesn't notice when you stopped. AI has no concept of Day 14. It

Maybe I've watched too many episodes of Black Mirror , or maybe I'm just afraid of the day this new form of consciousness gets the upper hand, but I genuinely feel uneasy whenever I intentionally leave out 'please' from a command like, 'Hey Google, please lower the volume.' The other day, I actually forgot my intended request right after the initial prompt, so I just said, 'Hi.' I’ve never had such an awkward conversation in my life. I need to pull the transcript, because all of a sudden Gemini was forcing random small talk and offering to tell me a random fact or two. Creepy... submitted by /u/Affectionate_Paint58 [link] [留言]

I often record my screen for clients to show what I’ve built or how something works. But almost always I end up writing detailed comments and explanations anyway, because they just blink and miss things in the video. So I thought it’d be cool to add freeze-frames with annotations right inside the video, to pause their attention and explain what's happening at that exact moment. I tried doing this in video editors, but it takes way too much time, so I hated the process. But the videos with freeze-frames actually worked great for my clients. I got way fewer questions from them. That’s why I decided to build a tool to add freeze-frames and annotations quickly and easily. I wanted it to feel as simple and natural as drawing in Excalidraw. Let me know if you find this useful, or if it feels unnecessary. You can try it here: https://framepin.com . There is a quick demo to save you time. No registration, everything works locally in the browser. submitted by /u/aksuta [link] [留言]

So for about 10 years I've been wanting this every time I'm on a scam website or news article that has no comment section or locked reddit thread. I've seen a couple similar attempts, but most of them focus on personal notes or having drawing features and things I'm not really interested in. This is just about letting users, customers and the general public communicate about URLs and sub-urls the websites themselves don't provide or won't provide the public the ability to comment on. In the extension you can see how many comments are on a URL and your notifications if anyone replied to you. There are some rules like everyone is only allowed one top level comment to reduce spam or potential bots. You can replay as many times as you want. You can also edit your top level comment, which will show last when it was edited. Here is the main list of reasons I created it: Warn people on scam websites when they go there they can just read the comments Comment on locked social media posts, x profiles, youtube videos, reddit threads etc.. Comment on news articles that have no comment section Comment on top of social media that would otherwise require an invasive account to comment. Almost all social media also has a url these days. This also works with any string so technically you can comment on anything including, addresses, businesses, cities. If you paste in a URL it will normalize it so you won't accidentally create a duplicate forum. On the PublicNotes.xyz homepage you can just create a forum based on anything. Feedback and bug notes welcome. Website: https://publicnotes.xyz Chrome extension: https://chromewebstore.google.com/detail/public-notes/keaopkaplnpjhccneageakejbbbeoncp?pli=1 submitted by /u/PandorasBucket [link] [留言]

The first electric Ferrari is already this year’s most divisive car. We asked three Italian auto industry professionals to explain where the EV’s design makes sense, and where it doesn’t add up.

I'm working on a self-hosted search service called Hister with the goal to reduce dependence on online search engines. Hister is a full text indexer for websites and local files which automatically saves all the visited pages rendered by your browser. It provides a flexible web (and terminal) search interface & query language to explore saved content with ease or quickly fall back to traditional search engines. I've been using it for a few months and as my local index is growing I can avoid opening google/duckduckgo/kagi - and even websites listed in results - more and more frequently. The initial reception is overwhelmingly positive with already more than 30 contributors and hundreds of contributions - perhaps you can find it useful as well. (Or at least have some constructive criticism =]) The code is AGPLv3 licensed, available at https://github.com/asciimoo/hister , read-only demo: https://demo.hister.org/ , website: https://hister.org/ submitted by /u/asciimoo [link] [留言]

Hello! I am Axel and I’ve been building Kanto, a small forum platform for niche communities. The main idea is that communities can allow normal posting or optional anonymous posting which is fully secure and private, while still requiring accounts behind the scenes. Users can create communities, start threads, reply, and browse communities around specific interests! :) I’m trying to keep it simple and privacy-friendly: no ads, no selling user data, and a cleaner experience than huge social platforms, not like the big american social media companies that sell your data. https://kanto.forum submitted by /u/axeeeeeel- [link] [留言]

Hey r/ML , I spent the last few months building a tool that hooks into PyTorch training loops to automatically detect and localize failures (vanishing gradients, exploding gradients, data anomalies). Along the way, I learned some things about training failure diagnosis that might be useful even if you never use the tool. The key insight: most training failures are local, not global When your loss spikes or vanishes, the natural instinct is to look at the loss curve. But the loss is a global aggregate — it tells you something went wrong, but not where . In my testing across hundreds of synthetic failure scenarios, the actual root cause is almost always localized to a specific layer at a specific step : Vanishing gradients: the failure starts at the deepest layer with saturated activations, then propagates backward Exploding gradients: the failure starts at the layer with the highest gradient norm, then propagates forward Data anomalies: the failure starts at the input layer, then corrupts everything downstream The trick is to monitor per-layer gradient norms and detect transitions (healthy → vanishing), not absolute values. What actually matters in gradient monitoring Most people monitor: - Loss over time (too global) - Gradient histograms (too noisy, too much data) - Weight norms (slow to change, lagging indicator) What I found works best: - Gradient norm transitions : "Linear_3 went from healthy (0.12) to vanishing (0.00003) at step 47" - First occurrence tracking : which layer failed first (this is usually the root cause) - Activation regime shifts : when activations go from normal to saturated/dead This is basically what NeuralDBG does under the hood — I open-sourced it recently and it's on PyPI ( pip install neuraldbg ) if anyone wants to try it. The key design choice was to extract semantic events (transitions) rather than raw tensors — this makes the output small enough to reason about. Practical takeaway you can use today Even without any tool, you can add th

https://preview.redd.it/035k5k1tl84h1.png?width=1122&format=png&auto=webp&s=459c430ea4a4b3fc667bc3f2e72ab47d8a380aa2 I asked gemini to expand my prompt for a video generator, but he had other plans for me. EDIT: Forgot to mention, using the PRO model, after around 15 seconds of him literally thinking and writing "expanding the prompt", he started generating the video... submitted by /u/ObjectiveOrchid5344 [link] [留言]

https://lifeappbybabs.netlify.app I want to see if it's worth investing in and buying a domain name submitted by /u/Miserable_Spirit3319 [link] [留言]

A $0 multi-model decision agent: three LLMs debate, Hermes judges, and it learns who to trust.

TL;DR : Built a RAG pipeline, allows you to upload docs and connect via API/MCP to service. need feedback if you find it useful or confusing: https://rig-rag.vercel.app I got into the whole AI, embeddings, etc workflow for another project of mine and while pretty fascinating how this all ties together, I thought that it is perhaps also useful to others who don’t want to rebuild the whole pipeline from scratch every time. (also, competition seems pretty costly). It is pretty simple: Make account, make memory space, upload your stuff, configure how you want it retrieved, setup scoped API key, connect your AI to the hosted MCP → done. RigRAG lets you: upload / index documents search them via a simple HTTP API or MCP optionally configure retrieval settings like top-k, candidate count, chunking, etc. Currently entirely free to use since I would want to gather feedback first if it's worth pursuing further before I spend too much time into it. I’d really appreciate feedback on it, if something is unclear to setup, should be improved etc. I’m not trying to do a big launch yet so this is more of a “please try it, and tell me what sucks or if its good” thing. Here the link again: https://rig-rag.vercel.app And for the MCP: https://rig-rag.vercel.app/api/mcp submitted by /u/Odysseyan [link] [留言]

为什么使用代理总弹出“安全验证”？深度解析 Cloudflare 拦截机制与避坑指南 在互联网开发、跨国办公或日常浏览中，使用代理（如 VPN、机场、Socks5、OpenVPN/WireGuard 协议等）已经是不可或缺的技能。 然而，许多人在开启代理后，访问国外网站（如 Dev.to、GitHub、Medium 等）时，频繁遭遇如下提示： Performing security verification This website uses a security service to protect against malicious bots. This page is displayed while the website verifies you are not a bot. 甚至更让人崩溃的是，有时候点击了验证码，它依然不断刷新，陷入 无限验证死循环 。这并不是你的系统或浏览器损坏了，而是代理网络的特性触发了现代 Web 安全防御机制。本文将从技术原理深入拆解这一现象，并提供切实可行的优化方案。 一、 核心原理：网站安全服务是如何盯上你的？ 现代网站大多会部署 Cloudflare（如 Turnstile 验证） 、Akamai、Imperva 等网络安全与防 DDoS 攻击服务。这些服务通过以下几个维度来评估访问者是“真实人类”还是“恶意机器人（Bot）”： 1. IP 信誉度（IP Reputation）与“连坐”机制 这是最核心的技术原因。代理服务商（特别是商业 VPN 或公共机场）所使用的 IP 地址，绝大多数属于 数据中心（Data Center）机房 IP ，而非普通家庭的 住宅（Residential）IP 。 高密度共用： 同一个代理 IP 节点上，可能同时有成百上千个用户在发起请求。 黑名单牵连： 如果该 IP 下的其他匿名用户正在使用自动化脚本抓取数据、进行端口扫描，或者发起恶意网络攻击，安全系统的风控引擎（如 Cloudflare IP Threat Score）就会瞬间拉高该 IP 的风险等级。当你恰好切换到这个“脏 IP”时，就会被系统无差别“连坐”，要求强制验证。 2. 被动指纹识别（Passive Fingerprinting）与几何特征 安全防御系统不仅看你的 IP 归属地，还会通过深层网络和浏览器几何特征来判断你的真实身份： TLS/SSL 握手特征（JA3 指纹）： 当你通过一些特定协议或混淆模式（如带有特定加密的 TCP 隧道）连接网站时，浏览器发出的 TLS 握手特征可能会发生形变。 TCP/IP 栈特征： 经过代理服务器的转发，数据包的 TTL（生存时间）、Window Size（TCP 窗口大小）等底层参数可能会与你浏览器宣称的操作系统（如 Windows 11 或 Ubuntu 24.04）的标准特征不匹配。 浏览器画布与几何指纹（Canvas/Geometry）： 浏览器的窗口大小、屏幕分辨率以及它们的比例，也是风控系统评估的重要指标。 自动化爬虫脚本（如 Selenium、Puppeteer）在启动时，常常使用死板的默认分辨率（如完美的 1024x768 或 800x600 ）。如果你的代理 IP 本身信誉度低，窗口又处于这些“机器人专属分辨率”下，或者网页窗口大小与物理显示器分辨率比例极其诡异（例如伪造环境时穿帮），就会直接触发拦截。 3. 环境与地缘标签冲突（以 Yandex 浏览器为例） 风控系统对你使用的浏览器品牌同样有一套风险权重评估。 如果你使用的是 Yandex 浏览器 或某些小众、经过重度隐私魔改的浏览器，在配合代理时会变得 极其难通过验证 。Yandex 浏览器虽然基于 Chromium 内核，但其内部由俄罗斯团队集成了大量独特的隐私保护技术与 Canvas 渲染机制，计算出的浏览器指纹非常非主流。 更致命的是 地缘标签冲突 ：欧美的主流网络安全公司（如 Cloudflare）对特定区域标签的客户端流量天然设置了更低的信任阈值。当你 用着 Yandex 浏览器 ，IP 却 挂着美国或日本的代理 时，这种“指纹与地理位置的剧烈冲突”在风控模型眼里极度反常，系统会判定该请求大概率来自自动化黑客工具，从而直接卡死验证。 4. 地理位置与行为“瞬移” 如果你的代理客户端开启了“负载均衡”或“定时自动切换节点”，可能会导致前一分钟请求来自日本，后一分钟请求来自美国。这种超越物理极限的“空间瞬移”属于高风险异常行为。此外，如果通过代码 瞬间改变 窗口尺寸，而非人类拖拽时产生的连续 resize 事件，也会被风控脚本捕捉到异常。 二、 实战优化：如何彻底摆脱“无限验证”死循环？ 要彻底解决或缓解这个问题，可以根据实际的使用场景，从 节点筛选 、 路由分流 以及 浏

The .txt File as the Soul of a Personal AI — FileRAG Memory Architecture By Dharanidharan J (JD) Full Stack & AI Engineer | Building Jarvix The Problem Nobody Talks About Every chatbot tutorial teaches you the same thing: history = [] history . append ({ " role " : " user " , " content " : message }) And that works — until it doesn't. After 500 turns, your dict has forgotten who the user is. After 1000 turns, you're hitting token limits. After a restart, everything is gone. Redis helps with persistence but still buries early facts under noise. Vector DBs help with retrieval but bloat storage and need infrastructure. What if the memory itself was just a file? The Idea Every conversation a user has gets distilled into a plain .txt file. That file is the brain. On every new query, a hybrid BM25 + semantic RAG retrieves the most relevant chunks from it and injects them as context. users/ └── jd.txt ← the soul file The soul file looks like this: [Turns 1-5] - User's name is JD, software engineer - Building FileRAG, a novel memory architecture - Uses Pop!_OS with Fish shell and NVIDIA GPU [Turns 6-10] - Has a cat named Pixel who distracts during coding - Paused TaskNest due to burnout - Now focused on AgenticMesh Human readable. Editable. Yours. Why This Is Different Most memory systems store messages . FileRAG stores a relationship . System What it stores Dict / Redis Raw message objects Vector DB Embeddings of messages FileRAG Distilled understanding of the user The longer you use it, the more the AI understands you — not because it has more messages, but because it has a better summary of who you are. The Architecture User message ↓ Topic drift check (cosine similarity) ├── Drift detected → distill current buffer immediately └── No drift → continue ↓ Hybrid retrieval (BM25 + ChromaDB) from soul file ↓ Inject context → LLM responds ↓ Append to turn buffer ↓ Every 5 turns → distill → append to soul file → update ChromaDB ↓ Emergency distillation on exit (SIGINT/SIGTERM)

로봇 두 대가 말 한마디 없이 방을 정리했다, 그런데 진짜 질문은 '어떻게'가 아니다 협업의 정의가 바뀌고 있다. 인간끼리도 아니고, 인간과 로봇도 아니라, 로봇과 로봇 사이에서. TL;DR : 피규어 AI의 휴머노이드 두 대가 언어 없이 2분 만에 침실 정리에 성공했다. 기술 자체보다 흥미로운 것은, 이 '눈치'가 어떻게 만들어졌는가이다. 로봇 협업이 인간 협업의 방식을 모방한 게 아니라, 아예 다른 방식으로 진화하고 있다는 신호다. 로봇 산업에는 잘 알려지지 않은 규칙이 하나 있다. 로봇을 한 대 잘 만드는 것보다, 두 대가 함께 작동하게 만드는 것이 기하급수적으로 어렵다는 것. 보스턴 다이내믹스는 수십 년 동안 혼자 뛰고, 혼자 문을 열고, 혼자 계단을 오르는 로봇을 만들어왔다. 테슬라의 옵티머스는 혼자 부품을 집고, 혼자 배터리를 나른다. 그런데 피규어 AI는 올해 다른 질문을 던졌다. "두 대가 서로 말을 하지 않아도, 협력할 수 있을까?" 그리고 최근 그 답이 나왔다. 2분이었다. 먼저, '눈치'라는 단어를 다시 생각해야 한다 우리가 일상에서 쓰는 '눈치'는 상당히 복잡한 인지 활동이다. 상대방의 행동을 보면서, 다음 행동을 예측하고, 내 행동을 조율하고, 충돌을 피하고, 빈틈을 채우는 것. 인간은 이걸 언어 없이, 심지어 시선 교환만으로 해낸다. 오랜 시간을 함께한 팀에서, 숙련된 주방의 요리사들 사이에서, 그리고 가족 사이에서. 그런데 이 능력은 학습된 것이지, 타고난 것이 아니다. 아이들은 눈치가 없다. 신입 직원도 눈치가 없다. 수백 번의 상호작용과 실수와 교정을 거쳐야 비로소 '눈치'가 생긴다. 피규어 AI의 휴머노이드 두 대는 이 과정을 어떻게 압축했을까. 보도에 따르면 이들은 사전에 언어 명령이나 역할 분담 지시 없이, 상대 로봇의 행동을 실시간으로 인식하고 자신의 다음 동작을 결정했다. 공간을 나눠 쓰고, 같은 물건에 손을 뻗지 않고, 한쪽이 멈추면 다른 쪽이 채웠다. 이것을 연구자들은 '암묵적 협업(implicit collaboration)'이라고 부른다. 쉽게 말하면, 로봇이 눈치를 배웠다는 뜻이다. 두 대가 함께 움직인다는 것의 기술적 의미 단일 로봇의 작동 원리는 비교적 단순하게 설명할 수 있다. 센서가 환경을 인식하고, 모델이 행동을 결정하고, 액추에이터가 실행한다. 루프가 하나다. 두 대가 함께 움직이는 순간, 루프가 두 개가 아니라 세 개가 된다. 로봇 A의 루프, 로봇 B의 루프, 그리고 A와 B가 서로를 환경으로 인식하면서 생기는 상호작용 루프. 이 세 번째 루프가 문제다. A의 행동이 B의 환경을 바꾸고, 그 변화가 다시 B의 행동을 바꾸고, 그 행동이 또 A의 환경을 바꾼다. 루프가 루프를 먹는 구조다. 이것을 중앙에서 통제하는 방식은 예전부터 존재했다. 공장 자동화에서 쓰이는 PLC(프로그래머블 로직 컨트롤러) 방식이 대표적이다. A는 1번 작업, B는 2번 작업, 충돌 시 A가 우선 — 이런 식으로 모든 경우의 수를 미리 프로그래밍한다. 정해진 공간, 정해진 물건, 정해진 순서. 공장에서는 작동한다. 일상에서는 작동하지 않는다. 침실은 공장이 아니다. 물건의 위치가 매번 다르고, 침대 정리와 바닥 정리가 동시에 일어나야 할 수도 있고, 하나가 예상치 못한 물건을 발견하면 계획 전체가 바뀐다. 규칙 기반의 중앙 통제로는 불가능하다. 피규어 AI가 선택한 방향은 분산 의사결정이었다. 각 로봇이 독립적으로 환경을 인식하고, 상대 로봇의 현재 상태를 하나의 입력값으로 받아들이면서, 스스로 다음 행동을 결정하는 방식이다. 중앙 관제탑이 없다. 각자가 판단하되, 서로를 인식한다. 이것이 인간의 눈치와 구조적으로 가장 유사한 접근이다. 2분이라는 숫자가 중요한 이유 2분. 이 숫자를 처음 들으면 "겨우 2분?"이라고 생각할 수 있다. 그런데 맥락을 알면 반응이 바뀐다. 로봇이 단독으로 침실을 정리하는 데 걸리는 시간과 비교해보자. 현재 가장 발전한 단일 휴머노이드 로봇들의 가사 작업 수행 속도는, 같은 작업을 인간이 하는 것보다 보통 3배에서 10배 느리다. 동작이 느린 것도 있

Production incidents almost never break in one place. The alert fires in one tool. The broken deploy is in Netlify. The suspicious change is in GitHub. The stack trace is in Sentry. The human context is in Slack. The runbook is in Notion. The "is this actually paging someone?" answer is in PagerDuty. A normal chatbot can sound helpful in that situation. It can say things like "you should check your recent deployments" and "look for related errors in Sentry." But that is not triage. That is a polished to-do list. I wanted something more useful: an agent that could go get the evidence, connect the dots across sources, show its work, and give an operator-grade answer grounded in real system data. The design constraint from the start was simple: no evidence, no answer. That became ReefWatch , a Coral-powered production triage agent built to investigate instead of improvise. It discovers the tools connected to a workspace at runtime, queries them as evidence, correlates records across systems, and produces a compact answer only when the facts support one. Coral became the backbone because it turns the messiest part of agent tooling into something the model can actually reason about: SQL . What This Guide Builds By the end of this route, you will have a blueprint for an agent that can: discover connected Coral sources at runtime query production systems through read-only SQL correlate evidence across code, deploys, errors, alerts, chats, and runbooks stream every query and row count into an inspectable UI run the same investigation workflow from a CLI when you want a scriptable path generate an incident report only when the evidence supports one stay focused with policy layers instead of a giant prompt blob In one sentence: ReefWatch is a Coral-powered investigation workspace that lets an agent discover connected tools at runtime, query them with read-only SQL, stream the evidence trail, and generate an incident report only when the facts actually support one. Why Coral B