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🔥 PostHog / posthog - 🦔 PostHog is an all-in-one developer platform for building s

GitHub热门项目 | 🦔 PostHog is an all-in-one developer platform for building successful products. We offer product analytics, web analytics, session replay, error tracking, feature flags, experimentation, surveys, data warehouse, a CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack. | Stars: 35,625 | 58 stars today | 语言: Python

2026-07-16 原文 →
AI 资讯

Claude can now use your 1Password credentials for you

1Password has launched a new browser integration for Claude that allows the Anthropic chatbot to access stored security credentials like usernames and passwords. The 1Password for Claude feature means that users can authorize Claude to complete multi-step tasks like booking travel and managing online accounts on their behalf without having to manually input their login […]

2026-07-16 原文 →
AI 资讯

OnePlus never had a chance in the US

Twelve years after the launch of the OnePlus One, OnePlus announced today that it has exited the United States. It's bittersweet, as the brand has been on a comeback tour of sorts with its excellent OnePlus 15 and widely praised (though still too expensive) OnePlus Open. The writing has been on the wall for a […]

2026-07-16 原文 →
AI 资讯

How AI Can Help You Improve Your Performance as a Developer

Why this matters Let’s be real — most of us don’t struggle because we “can’t code”. We struggle because: we waste time on repetitive tasks we get stuck on small bugs we context-switch too much we overthink simple problems That’s where AI actually helps. Not as a replacement — but as a performance multiplier . 🤖 First, what AI is actually good at AI is not magic. But it’s really good at: generating boilerplate explaining errors suggesting improvements summarizing docs speeding up repetitive work 👉 Basically: saving your mental energy ⚡ 1. Write code faster (without burning out) Instead of writing everything from scratch: // prompt idea " create a custom React hook for localStorage " You get a solid starting point instantly. 👉 You still review it 👉 You still understand it 👉 But you don’t waste time writing boilerplate 🐞 2. Debug faster Instead of Googling for 20 minutes: Error: Cannot read property 'map' of undefined You ask AI: 👉 It explains the issue 👉 suggests fixes 👉 shows edge cases Example mindset shift Before: search → open 5 tabs → read → test → maybe fix Now: ask → get explanation → apply → move on 🧠 3. Learn way faster AI is like having a senior dev on demand. You can ask: “Explain React Server Components simply” “When should I use memo?” “What’s wrong with this pattern?” 👉 Instant explanations 👉 Real examples 👉 No fluff 🔄 4. Automate boring tasks Things you shouldn’t waste time on: writing regex generating types creating repetitive components converting data formats 👉 AI handles these in seconds 📚 5. Write better documentation Most devs hate writing docs. AI helps you: generate README files write comments document APIs 👉 Your project becomes easier to understand 👉 Your team moves faster 🧩 6. Break down complex problems Instead of getting stuck: "build a dashboard with auth, charts, and API integration" Ask AI to break it down: 👉 smaller steps 👉 clear structure 👉 less overwhelm ⚡ 7. Stay focused (this is underrated) Biggest hidden benefit: 👉 less context swi

2026-07-16 原文 →
AI 资讯

The SQL injection bug your code review keeps missing

Every TypeORM project I've worked on grows the same few dangerous lines. I got tired of catching them by hand, so I wrote a linter that does it for me. I was reviewing a pull request a while back and nearly scrolled past this line: await manager . query ( `SELECT * FROM users WHERE id = ${ req . params . id } ` ); Looks fine at a glance. It's a SQL injection hole. The id comes off the request and lands directly in the query string. The thing that bugged me is that I only caught it because I happened to be reading carefully on that line, that day. Review catches this sort of thing a lot of the time. "A lot of the time" is how these end up in production. It's usually not only injection either. The same projects tend to collect a few other habits: synchronize: true in a data source config. It rewrites your schema on startup and can drop a column on the next deploy. A QueryBuilder delete() or update() that hits .execute() with no .where() . Forget that one line and you've changed every row in the table. Three or four writes in one function, none in a transaction, so if the second throws you're left with half-written data. On multi-tenant apps, a query that forgets the tenant filter. That's how one customer sees another customer's data. None of these really need a person to catch them. They're mechanical. A linter can do it on every commit. So I built one. eslint-plugin-typeorm-enterprise npm install --save-dev eslint eslint-plugin-typeorm-enterprise // eslint.config.js const typeormEnterprise = require ( ' eslint-plugin-typeorm-enterprise ' ); module . exports = [ typeormEnterprise . configs . recommended ]; Now those patterns are lint errors. Ten rules today, split into configs ( recommended , strict , performance , multiTenant ): raw SQL, interpolated and concatenated SQL, unsafe QueryBuilder deletes, EntityManager raw queries, writes outside a transaction, tenant scoping, and a nudge to stop counting rows when you only need to know one exists. Two things I was picky

2026-07-16 原文 →
AI 资讯

Stuck in the Loop: Why AI Agents Retry, Oscillate, and Never Finish

An agent moving through a multi-step task needs two things it doesn't automatically have: a reliable way to know when the task is actually finished, and a reliable way to recognize when its current approach isn't working. Without both, the agent has no internal alarm bell. It just keeps acting — and if the same action keeps producing the same unhelpful result, nothing tells it to stop, change course, or ask for help. This isn't a minor implementation detail. It's a structural gap in how most agent loops are built: observe, decide, act, observe again. That loop has no natural exit condition unless one is explicitly designed in. Pattern One: The Retry Loop : The retry loop is the simpler of the two failure modes. The agent takes an action, it fails, and the agent tries the exact same action again — sometimes with trivial variation — expecting a different outcome. A few reasons this happens: Misread failures : The agent doesn't correctly interpret why the action failed, so it can't adjust its approach. It just repeats the attempt. No failure memory : Without a persistent record of "I already tried this and it didn't work," the agent has nothing to check against before trying again. Overconfidence in the plan : If the agent's internal reasoning treats the original plan as correct, it may conclude the execution was the problem, not the plan — and simply re-execute. The result is a kind of insanity loop: identical input, identical output, repeated until a turn limit, budget cap, or timeout finally intervenes from the outside. Pattern Two: Oscillation : Oscillation is subtler and, in some ways, more dangerous, because it can look like activity rather than failure. The agent doesn't repeat the same action — it alternates between two (or more) states, undoing its own progress each cycle. A classic example : An agent editing a file makes a change, then in a later step "fixes" that change back to something close to the original, believing it's correcting an error. The next cyc

2026-07-16 原文 →
AI 资讯

BIP-110 Explained for Developers: How Bitcoin Soft Forks Actually Work

Published to Dev.to — Bitcoin Development Series, Part 1 of 4_ Bitcoin is heading toward an August 2026 deadline for BIP-110, a proposed temporary softfork that would restrict Ordinals-style arbitrary data from being embedded in transactions for one year. As of today, miner signaling sits at effectively zero. The proposal is almost certainly going to fail but the mechanics of why and how are worth understanding if you work anywhere near the Bitcoin protocol. This post walks through how soft fork activation works, what BIP-110 specifically proposes, and how to inspect miner signaling yourself with code. What Is a Soft Fork? A soft fork is a backward-compatible change to Bitcoin's consensus rules. Nodes running old software still accept blocks from nodes running the new rules — but not vice versa. This is what makes soft forks safer than hard forks in a permissionless network: you do not force everyone to upgrade on day one. Hard forks, by contrast, change rules in a way that causes old nodes to reject new blocks entirely. They require near-universal coordination, which is why Bitcoin has avoided them. How Soft Fork Activation Works: BIP 9 The dominant activation mechanism used since 2016 is defined in BIP 9 . The process works like this: A proposal is assigned a version bit (bit 0–28) in the block header's nVersion field. Miners signal readiness by setting that bit in blocks they produce. Activation requires 95% of blocks in a 2,016-block retarget window to signal support. There is a starttime and a timeout . If the threshold is not met before timeout , the proposal fails and is discarded. # Simplified BIP 9 state machine logic THRESHOLD = 0.95 # 95% of blocks in a retarget window WINDOW = 2016 # one retarget period def check_activation ( signaling_blocks : int , total_blocks : int ) -> str : ratio = signaling_blocks / total_blocks if ratio >= THRESHOLD : return " LOCKED_IN " # activates after one more window return " STARTED " # still counting print ( check_activati

2026-07-16 原文 →
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