Claude Code

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

The real reason coding agents fail in real repos — and it's not the model

Most coding agent failures I see aren't model failures. They're repo context failures. The agent doesn't know what to read first, what the validation actually checks, which decisions are already made, what "done" means on this team. So it guesses. After tracking hundreds of these failures, I've gotten pretty good at predicting where agents will stumble. The pattern is always the same: the repo has zero structured context for anything that isn't the code itself. I've been building a repo-level harness-experimental that forces structure into those gaps -- CLAUDE.md, architecture notes, test matrices, decision records. Curious what gaps your agents hit most often? What's something they always get wrong that you'd never think to explain in a prompt? submitted by /u/tickettodamoon [link] [comments]

anyone using rtk with claude code ? Are you really saving tokens??

rtk-ai repo has 56.6k stars and claims they save 60-90% tokens so to give it a try i started using it , Here is the feedback My one day savings 0.3% tokens . Reads are highly consuming so i added on claude to always use ls (supported by rtk claims 60% savings). Had 300+ reads with rtk 0% tokens saved . Am i using wrong ? Are you aware of repos that really saves tokens submitted by /u/EcstaticLime2672 [link] [comments]

Deepeseek inside claude code -Easist way

For those who cant afford claude models and wanna use claude code, deepseek v4 pro is closest best and cheapest option. How to use deepseek API inside claude code (easist way ever): We will use AI to replace AI. Just feed your existing claude code this prompt "Yo Claude, you’re expensive af 💀 Do everything needed to fully switch Claude Code to DeepSeek API automatically. Set up the complete settings.json config, API integration, model selection, base URL, env variables, testing, debugging, and optimization for low cost + strong coding performance. Use this DeepSeek API key: "sh......................" Make it fully working, minimal, and production ready." Thats it! Thank me later! submitted by /u/Agreeable-Pen-9763 [link] [comments]

I built a local mission control for Claude Code — it auto-stops when you hit your budget

Been using Claude Code heavily and kept running into the same problem — sessions would run long with no visibility into cost until it was too late. No built-in way to set a hard stop at $5 or 10k tokens. So I built AgentFleet — a local web UI that wraps Claude Code (and Codex) with: - Live terminal streaming in the browser via xterm.js so you can watch what the agent is doing in real time - Automatic session stop when you hit a USD or token budget limit - Session history persisted to local SQLite so you can review what happened after a session ends - Works with any shell command, not just Claude Code Everything runs locally — no cloud, no accounts, no data leaving your machine. It's an MVP so there are honest limitations (token count is estimated, PTY sessions don't separate stdout/stderr). But the budget enforcement works and has already saved me from a few runaway sessions. Repo: https://github.com/akhilsinghcodes/agents_fleet Happy to answer questions about how the PTY streaming or budget enforcement works under the hood. submitted by /u/mahsin09 [link] [comments]

I made Claude Code actually understand what it’s committing — not just wrap git commit -m

If you use Claude Code for development, you’ve probably seen it generate commit messages like “update files” or lump 3 unrelated changes into one commit. I built git-courer to fix that. It’s a Git MCP server that gives Claude Code 17 structured tools to work with your repo — and before Claude writes anything, Go analyzes the diff semantically and tells it exactly what changed: new function, modified signature, breaking change, deleted type. Claude only writes the prose. It doesn’t guess the commit type. Real output it generated: fix: Fix MCP server connection handling WHY: The previous implementation lacked proper error handling for connection failures, causing silent failures when the local LLM backend was unavailable. WHAT: • Added connection timeout logic • Implemented retry with exponential backoff One staged set = one commit, always. No more giant commits with everything mixed together. Setup is one command: git-courer mcp setup — Claude Code is one of 13 preconfigured clients. Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

Follow-up: I talked my manager out of ranking engineers by AI usage. Now the harder question: how do you actually show ROI on AI spend?

Follow-up to my post last week about being asked to stack-rank engineers on AI usage. Thanks to everyone who weighed in - the consensus (token usage is a garbage metric that just rewards waste) gave me the ammo to push back, and I think I managed to dodge the stack-rank for now. But it surfaced the real question underneath, and honestly it's harder: "Fine - but show me the ROI on what we're spending on AI. Don't tell me it's helping, show me." And I don't have a great answer. The spend is real and growing, "trust me, it makes us faster" doesn't fly with finance, and the obvious metrics are all flawed: tokens measure cost not value, velocity's noisy, "lines of AI code" is not very meaningful. So, genuine question for teams further along: How do you actually demonstrate ROI on AI coding spend to non-engineers (finance, leadership)? Has anyone tied AI usage to real outcomes (shipped features, cycle time, quality) in a way that holds up to scrutiny? Or is everyone just hand-waving "it's obviously worth it" and hoping nobody asks? Trying to find something defensible before I'm asked to present numbers I don't believe in. submitted by /u/darren_eng [link] [comments]

Claude Code pet with context window awareness

https://preview.redd.it/jyhqqnf0qe4h1.png?width=900&format=png&auto=webp&s=dbc00b2eff253b82d8d334ff1cee358864d795af Hey all, thought I'd share a fun side project. 🐾 claude-pet - a tiny animated pet that lives in your Claude Code statusline. https://github.com/AmirYa412/claude-code-pet It has 3 moods that track your context window usage, a gentle nudge to /compact before you hit drift. One install command(in README.md) sets up the full statusline: pet · model · cwd · git branch · ctx% · cache Pure Bash no forks, no temp files, no dependencies beyond jq. Weighted-random frames so it never loops. Interactive demo here: https://amirya412.github.io/claude-code-pet/ Enjoy! submitted by /u/Saidden [link] [comments]

New to Claude - Having fun building a website

I (54M) have never built a website so I thought using Claude to do it would be a fun way to learn both of those things (I have no experience with code or anything like that really). I'm about 12 hours in and roughly half way I think. The problem is I don't think I'm really learning anything, I'm just typing, clicking, finding and replacing where it tells me too. I'm also probably making it way harder than it needs to be, I suspect Claude can do a lot of the things it's getting me to do lol Any advice on how to learn this stuff easier and more effectively? submitted by /u/Great-Address-8344 [link] [comments]

Claude has quietly become my main coding partner

I use it every single day now. For debugging, explaining concepts, writing boilerplate, and thinking through architecture. It just feels way more reliable and thoughtful than GPT-4o lately. Still use other tools too, but Claude has become the default. Anyone else using Claude as their primary AI for development work? submitted by /u/Real-Question-3050 [link] [comments]

Opus 4.8... what exactly is the improvement? Because it seems exactly the same, and these new versions never seem to solve the problems of: memory, context, understanding what we want, etc.

Hi, Been using CC for about a year, made a bunch of trash and a few working apps. But the issue is always basically the same. Claude doesn't remember what I want, it forgets what I've asked for, forgets guidelines that I've set. Commit to memory? It doesn't check. Write docs, comment code, extract methodology and ask it to stick? Sure, maybe per-prompt it might do it, key word being "might". It seems to me that this problem will never change with coding bots. It will always forget, it will never have enough context, it will never be able to store all the information the way a human mind can. If you want to use it effectively, you have to slow shit way down and literally map out every single thing you want it to do PER PROMPT. You cannot talk to it like a normal person, which is to say you cannot give it simple instructions and expect it to have context on a conversation you had 2 days ago where a problem was solved and you want it to use the learnings from THAT solution into its current task. Its not that Opus is "bad", its just that even though it sounds like a real human being when you talk to it, it is like a much much dumber version. Or rather a version of a human being that cannot remember anything, that forget things it learned 2 days ago. And unless YOU are conscious enough to remind it of every little thing it learned and how you want it to apply that knowledge to future tasks, you're going to run into the same problems over and over when developing. I'm not sure if I'm making sense here, but it's very frustrating and I don't think it's ever going to get better. In fact, I can't really tell what improvements there are from version to version. The speed I develop with seems to be more reliant on my ability to REMIND CC of exactly what I want it to do. Which means I have to paste super long prompts verbatim every single time that reiterate guidelines we established in previous sessions. Anyway... a bit of a rant, not sure if I explained it correctly. Just wanted to share. submitted by /u/yallapapi [link] [comments]

I made a thing to share how I built something with Claude Code, not just the final result

I've been seeing job applications and startup accelerators (like YC) asking for transcripts of vibe coding sessions as part of the process. I found the current experience of /export command lacking in capturing all the details. So I built VibeViewer. You drop a Claude Code transcript and it turns into a clean, replayable trace at a shareable link. Whoever you send it to can step through the whole session at their own pace. How it works: Drop your local .jsonl session file, or a .txt from /export if you don't want to dig for the file. Install plugin if you want it automatically uploaded Get a link in a few seconds, no account required Secrets get redacted on upload (transcripts are full of keys and tokens) Subagents are captured and replayable too, not just the top-level run Here's a live example so you can poke around without uploading anything: https://vibeshub.ai/t/7ntgpt45el And to try it with your own session: https://vibeshub.ai/vibeviewer Would love feedback, especially on the replay UI and on what would make you want to share one of your own sessions. What's missing? submitted by /u/bhavya6187 [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

Puppetmaster dramatically decreases token costs + increases context

Puppetmaster is an orchestrator + router that sits on top of the agent CLIs you already pay for (Cursor, Claude Code, Codex, OpenAI) or a plain shell when there's no harness at all. You hand it work, and it routes each task to the cheapest model that can actually do it, runs the workers as independent processes, and stores everything as durable typed state instead of one giant transcript. This is the "context-hack" Puppetmaster graphs your directories and prevents context stretching between agents. https://github.com/professorpalmer/Puppetmaster submitted by /u/ProfessorPalmer [link] [comments]

Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible. There was this idea that employees would engage in what we call "token maxing," where you use as many tokens as possible from your AI API. Companies like Meta and Amazon have even created internal leaderboards tracking how many AI tokens employees are using. The people using the most tokens are seen as the most AI-forward, the most AI-deployed — the ones who are going to get recognized, maybe even promoted. And this has resulted in extraordinary costs on the AI front. Now we're starting to see the next phase of this, Scott, where companies and their executives are beginning to realize: this is a little expensive. So the question becomes — at what point will AI actually pay off? I'll pose that question to you: at what point is it too much? SCOTT: I think we're already seeing hints of it, and I think it comes down to incentives. You were talking about how companies are trying to incentivize people to use AI more — and that's kind of an interesting part of the ecosystem right now. The adoption layer is trying to get people to use it, and companies have put in place the incentives to do that. But there was a recent survey by a professor at MIT who found that about 5% of the projects people are using tokens for can actually be connected by CFOs to some sort of return. So while I think they're really intoxicated by it — and talking about AI as much as you can in your earnings call is like adding "dot-com" back in the '90s — I think you're already starting to see some fatigue. And I think the AI companies are trying to get public as quickly as possible to raise that cheap capital before things start to — I don't want to say unwind, but... You can see how the string gets pulled here. A large company, a CEO who has a lot of credibility in the industry, just comes out and says: "We're dramatically scaling back our AI investment. Let's be honest, folks — we're just not seeing the return we'd initially hoped." And then Nvidia reports its first miss. Nvidia has beaten its estimates 15 quarters in a row. Nvidia's first miss probably takes the entire market down five or ten percent. You are seeing some productivity gains from this and quite frankly, they look as dramatic, if not more dramatic, than the internet. But look what happened in 2000. This definitely does feel like '99. And I'm waiting for the first CEO to come out and say we have to get procurement involved and dramatically scale back our expenses. I don't think it's that romantic, honestly. I think it's just going to be a traditional Fortune 500 company that starts the narrative: okay, this has been fun, but we have to dramatically decrease our AI investment because we're not seeing the ROI we'd anticipated. ED: Yeah. I mean, we heard a quote this week from the CEO of Match Group — not a huge company — but he said AI is costing them $5 to $10 million a year, and his exact words were: "I think we're benefiting from it, but it's hard to feel." So that's not great if we're supposed

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [link] [comments]