标签：#RAM

This week's tooling landscape is defined by two themes: cost compression on capable models and protocol-level standardization across agent runtimes. Gemini 3.7 Flash cuts inference spend while measurably improving first-pass code accuracy, and the AI SDK's ACP harness layer is quietly making multi-agent wiring less of a bespoke nightmare. Here's what's worth your attention. Gemini 3.7 Flash launches with coding performance gains Gemini 3.7 Flash ships at half the cost of 3.6 Flash with benchmark improvements that actually map to real workloads: FrontierCode jumps from 34.4% to 43.6%, and document reasoning on GDP.pdf goes from 22.0% to 34.0%. These aren't marginal deltas—a 9-point gain on code generation means materially fewer retries in agentic pipelines where each failed generation compounds latency and cost. For teams running Flash in production for code generation or document extraction, the math is straightforward: same API surface, half the token cost, better first-pass accuracy. Introductory pricing holds through year-end, so the window to lock in the savings is finite. Verdict: Ship. Drop-in swap via the Gemini API—no config changes required. If you're already on Flash for coding or document processing workloads, migrate now. The performance gains on code generation are large enough to reduce retry loops in multi-step planning tasks, which compounds into real infrastructure savings at scale. GLM 5.2 free for eve agents through August 27 Z.ai's GLM 5.2 is a 1M-token open-weights model now set as the default on eve agents, with free access through Vercel's AI Gateway until August 27. The 1M context window is the practical differentiator here—it's large enough to hold entire codebases in context for generation tasks that would otherwise require chunking or retrieval. The cost is zero during the trial window, and the integration is a one-line config change: set model: "zai/glm-5.2" in agent/agent.ts or run eve set --model zai/glm-5.2 . That's a trivially low bar

"Graphify turns codebases into queryable knowledge graphs. We designed a layered monorepo wiki extension — per-layer .graphify/ folders with staleness hooks — that doesn't exist yet. Here's the full spec and how to get 90% of it right now." Transparency note: This post describes a design pattern we invented on top of graphify , an existing open-source tool. The core tool is real. The layered wiki structure and .graphify/ folder convention described here are not official graphify features — they are a proposal. We'll clearly mark every invented part. The "how to approximate it today" sections use only real, working graphify commands. The Problem: One Graph, Many Layers If you haven't used graphify yet, the short version: you run /graphify . inside Claude Code (or graphify . --wiki from your terminal), and it turns your entire codebase into a queryable knowledge graph. Claude can then answer questions like "how does the checkout flow work?" or "what calls PaymentService?" with file-and-line citations instead of hallucinated guesses. It works brilliantly for single-service repos. But for a monorepo that looks like this: my-repo/ ├── frontend/ ← TypeScript / React ├── api/ ← Java / Spring Boot ├── services/ │ ├── order-service/ ← .NET / C# │ └── notification-worker/ ← Python └── database/ ← SQL stored procedures …you hit a wall. Run graphify . at the root and everything lands in a single flat graphify-out/ folder. The community articles generated by the --wiki flag end up mixing TypeScript React components with Java Spring controllers with SQL stored procedures. When you're deep inside the frontend layer fixing a component, Claude is also loading a wiki article about your database trigger — noise you don't need. The real question: what if each layer had its own scoped graph and wiki, colocated with the source it describes? The Concept: Layered Graphify Wiki ⚠️ Everything in this section is a design proposal — not official graphify. The folder names, behaviours, and some

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Your crawl job finished successfully. That doesn't mean it got the data. Every scraping pipeline has a monitoring dashboard, and every monitoring dashboard has the same blind spot: it tracks whether requests succeeded, not whether the content that came back was real. A job that completes with a wall of green 200 status codes looks healthy. It can also be quietly wrong, page after page, for weeks, because a 200 response only tells you the server accepted the request. It says nothing about whether you're looking at the actual page or a version built specifically for visitors the site doesn't fully trust. That gap between "the request succeeded" and "the data is correct" is where most silent pipeline failures live, and it's getting wider as anti-bot systems get more sophisticated about what they serve instead of an outright block. What a "successful" response can actually contain A block used to be simple to detect: a 403, a 429, a connection reset. Modern anti-bot systems increasingly prefer a different approach, because an obvious block tells the requester exactly what happened and invites a fix. A soft block, served with a 200, doesn't. In practice, that 200 can be a challenge page, an interstitial that looks like real content in the raw response but is actually a JavaScript-driven verification step (a "just a moment" style page, a hidden CAPTCHA iframe, a redirect loop disguised as a normal page load). It can be a cached fragment, an old snapshot of the page served to anything that looks automated, so the price, availability, or listing you scraped is stale even though the request itself worked fine. It can be an empty state, a search results page or listing that legitimately returns "no results" to a request pattern the site doesn't recognize, even though a real visitor would see dozens of items. And increasingly, it can be a partial HTML shell: the server response contains the page skeleton, but the actual content only renders after JavaScript executes in a real

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TL;DR Today, there are numerous AI models, and it's important for businesses to be able to...

Hoisting in JavaScript is the engine’s behavior of moving declarations to the top of their scope (global or local) before execution. Because of hoisting, you can reference functions or variables in your code before the lines where they are defined. 1.Function Declaration Function declarations are hoisted in their entirety—both the declaration and the body. This means you can call a function before it appears in the source code. hello (); //Output: hello! function hello (){ console . log ( " hello! " ) } 2.var Declaration When you use var, JavaScript hoists the variable declaration, but not its assignment. Until the execution line reaches the assignment, the variable holds undefined. console . log ( num ); //Output: undefined var num = 10 ; console . log ( num ); //Output: 10

Hello World! - Building a Project While Fighting Shiny Object Syndrome Let's start simple. What is "Shiny Object Syndrome"? Here is the definition pulled straight from Wikipedia: Shiny Object Syndrome is the situation where people focus undue attention on an idea that is new and trendy, yet drop it in its entirety as soon as something new can take its place. In my own words, I would describe it as chasing the novelty and the rush of starting a new project only to lose interest when I hit the not-so-fun parts. Why does that happen? I don't know. My guess would be that I have a lot of ideas that I want to see tangible results from fast . Like, for example: I want to see my app right in front of me in one or two sessions at most. I have a lot of energy for one week straight to work on my new idea, and then I lose interest at the first boring part I encounter. Very valid reasoning, but in the end, I'm left with a bunch of unfinished projects and feeling worse than when I started. This is why I'm here: to share my progress as I try to overcome SOS. I think I perform better when I have someone watching me, waiting for my results, or when I have a real deadline that isn't enforced only by myself. I need the consequences and the pressure to commit. So, now that you know what SOS is and why it sucks, let's see how to fix it. In front of me is one of my latest Shiny Objects (SO), and I've decided that I will apply these next steps to finish it before starting on a new SO. Here's the game plan: Open the Shiny Object. If I started working on it already: document a piece of the finished work every week. DO NOT START WORKING ON THE NEXT PART UNTIL ALL FINISHED PARTS ARE DOCUMENTED HERE. Plan for the next steps of the SO. Implement them (write notes on the changes and decisions taken while implementing). Document them here. Now that we have a vague plan of what we are going to do, let me tell you about the Shiny Object in question: It is a personal file drive where users upload fi

why do i find coding so diffiult. I just can't get myself to code. I open a video watch like 2 mins and get distracted. whenever i think of it , my throat muscles tighten. Please help me get started on this journey. Thank you . submitted by /u/WheelOk4703 [link] [留言]

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While mass-producing web tools with an AI, I've changed how I lock the design in three stages. The previous post I wrote about that got this comment: "I'd like to see the JSON approach and the design-system approach side by side." Taken at face value, I should just put the two side by side. But first, let me add a short preface. I don't want to frame this as "the JSON approach versus the design-system approach." When I called the JSON approach a "failure" in that post, I didn't mean the method is inferior; I meant it didn't suit my particular set of tools. A page made with the JSON approach does look thin. But where that thinness comes from is easily misread. Whether the design drifts and whether it looks rich are decided separately. What stops the drift is locking the design; whether it looks rich is how much you build out. What locking with JSON removes is drift in the items you specified in the schema. Whether the screen becomes rich, on the other hand, is determined by how much you've built out the machinery that turns that JSON into a screen. So it isn't that locking with JSON is what made it look like a spreadsheet. In the previous post, too, I wrote that fattening the schema and the renderer does increase the expression itself. But that came with a caveat: past a point, it heads toward rebuilding HTML and CSS by hand. What I really want to check is one step past that. If the template sets the ceiling on expression, then building out the JSON side's template as much as the current one should produce the same screen. So what does that build-out demand? I actually built it and measured. I'll share the result, along with the JSON-approach and design-system-approach screens placed side by side under matched test conditions. I'll admit up front: at the time, I chose the design system without running this comparison. So this is me building the road I didn't take, after the fact, and measuring what that cost consists of. Same order, same one-shot So that the comparis

We're back with another DEV Weekend Challenge, a short bite-sized challenge planned to fit into your...

Set a timer, run and submit your code if it's ready to rank on leaderboard. submitted by /u/capitanturkiye [link] [留言]

Full disclosure, we're the GitKraken team, and one of our devs has been tracking model benchmarks for a while now. Sharing because the trend line surprised us and figured this sub would have opinions. The frontier labs (Anthropic, OpenAI) keep trading the top spot by a point or two every couple months. Nothing new there. What's actually moving is the open weight tier underneath them: GLM went 5.1 to 5.2 and picked up 11 points of intelligence in about two months Kimi K3 picked up 13 points over K2.6 in three months and is now sitting in 4th place overall, a few points off the leaders Deepseek's V4 Flash 0731 update leapfrogged their own V4 Pro model, and it was a fine tune, not a full retrain Kimi K3 is popular enough right now that some providers are rate limiting or waitlisting it. Deepseek's approach (fine tuning an existing model instead of training a new one from scratch) seems like the more interesting engineering story here, honestly, more than the leaderboard position itself. Curious what this sub is actually running day to day. Anyone switched their default agent model in the last quarter because a newer open weight release actually caught up, or is everyone still defaulting to whatever frontier model they started with out of inertia? submitted by /u/GitKraken [link] [留言]

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A question I've been thinking about after discussing AI coding agents with several developers: Is passing the test suite enough to prove that an AI agent made the correct engineering decision? I don't think it is. And this isn't just a theoretical concern. Modern coding agents are increasingly working at the repository level rather than generating isolated code snippets. OpenAI's Codex documentation, for example, describes using repository-specific AGENTS.md instructions to tell the agent how to navigate a codebase, run tests, and follow project practices. Anthropic similarly describes Claude Code searching codebases, tracing dependencies, editing multiple files, and working with CI failures. ( OpenAI ) That changes what "correctness" means. Consider a simple scenario A project starts with: Architecture v1 API ↓ Service ↓ Database An AI agent learns this structure and implements a new feature correctly. The tests pass. Then the architecture changes: Architecture v2 API ↓ Event Bus ↓ Services ↓ Database The same task is requested again. If the agent continues following the old architecture, its code might still: compile, pass existing tests, satisfy the visible functional requirement, but still be wrong for the current system . This is the distinction I'm interested in: Code correctness ≠ Contextual correctness The Benchmark Problem Traditional coding benchmarks generally provide: Repository + Issue ↓ Agent ↓ Patch ↓ Tests / Evaluation This is valuable. SWE-bench, for example, was designed around real GitHub issues and repositories, and OpenAI created SWE-bench Verified with human validation because benchmark quality itself affects what we conclude about model capability. ( OpenAI ) But there is another dimension worth testing: What happens when the context changes? Recent research is already moving in this direction. SWE-ContextBench evaluates whether coding agents can reuse relevant experience across related tasks, while SWE-Explore focuses specifically on reposito

The social media giant says it was time for a sharper and more modern look after a decade.

Instagram has unveiled a new wordmark, moving away from the recognizable cursive typeface it's used over the last decade. The updated wordmark is a strange mix of half-cursive half-print that's somehow less legible than its predecessor. Like, that totally says "Instagzam," right? "The wordmark at the top of the app hasn't changed in 10 years, […]