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

Building the harness around our coding agents: eight failure modes, eight pillars

We ended up building two products: the software we ship, and the system/harness around our agents that makes them useful in building the thing we ship. A harness is the durable layer around a model: instructions, tools, permissions, context, and verification. Claude Code and Codex are harnesses in this sense. Each wraps a model with a system prompt, a tool surface, a permission model, and an execution loop. Anthropic and OpenAI own that layer. We own the next layer up: the workspace where agents do product work alongside us, with our files, tasks, diagrams, diffs, and decisions. This layer carries the knowledge we have accumulated: how we build things, what we already decided, what is connected to what, where the agent is allowed to act, and how it checks its own work. We identified eight coding agent failure modes that kept showing up across our sessions. Each one got its own pillar that we are continuing to invest in: * Doesn't know our codebase, rules, decisions, or conventions → **Context** * Can't traverse the links between artifacts that already exist → **Provenance** * Can't act on the world or observe what it did → **Capability** * Reinvents how to do every task → **Workflow** * Does something dangerous because nothing stops it → **Restraint** * Hallucinates "fixed" without proof → **Verification** * Can't show results back to us in a useful form → **Visual interface** * We can't keep track of work happening across many agents in parallel → **Coordination** For example, with Verification. The agent hallucinates "fixed" without proof . We write the failing test before writing the fix, so the bug has a reproduction the next agent can rerun. If the agent cannot show the change works end-to-end, it is not done. Or the agent works for hours and "fixes" the solution while breaking 2 other things or re-architecting 3 subsystems. We require full test case completion. The full writeup with diagrams and links to our actual harness dot md is in the comments. What other coding agent failure modes / harness pillars are you addressing for yourself / team and how?

I made a Free and Open Source Next.js SaaS Boilerplate for Claude Code. Built with Next.js + Tailwind CSS + Shadcn UI. Features include Auth, Multi-tenancy & Team Support, Roles & Permissions, MFA, User Impersonation, Landing Page, I18n, DB, Logging, Testing. GitHub in the comments.

Hey everyone, I just open-sourced a Next.js SaaS Boilerplate built to pair with Claude Code. The idea: give Claude a well-structured, opinionated codebase to work from so you can ship a SaaS way faster than starting from scratch. **Stack:** Next.js + Tailwind CSS + Shadcn UI GitHub repository: [https://github.com/ixartz/SaaS-Boilerplate](https://github.com/ixartz/SaaS-Boilerplate) **What's included out of the box:** * Auth + MFA * Multi-tenancy & Team Support * Roles & Permissions * User Impersonation * Landing Page * I18n * Database * Logging * Testing I'm also totally open to feedback and suggestion.

Antigravity to video and Claude to plan

I am broke and can't afford tokens to use Claude but oh em gee he's a beast at picking apart code and really nailing down how to get the ides to function correctly. Trying to make a custom LLM wrapper with a custom memory architecture. Claude looks over code and my usage limit skyrockets by 25 percent and I'm not even having the ide truly code yet. Needing to really figure out how to implement vision action loops and best way to pull up relevant memories with every prompt/task. Figure out a way to inject new context or relevant context at each step of a long task. I'm hoping to eventually create an ambient intelligence in my phone, PC, tv, etc. any ideas on what kind of architectures I could use to have it be able to jump from device to device without losing context and be able to customize my environment/give me real time information on things on my environment? Pretty sure the ad network that's in everything is already doing something similar just don't know if it's intelligence at the end user device or at a local node. Anyways Claude is picking apart the ide like a beast! It's kind of awe inspiring to watch an 'artificial' intelligence create working valid code like it's nothing or make suggestions and improvements to design docs and programming guides.

i benchmarked Anthropic's tool-search-tool head to head against our own MCP gateway on Opus 4.7. ours held up noticeably better

i'd been running Claude Code with a long list of MCP servers connected. Linear, Notion, GitHub, Slack, a few internal ones. and i was pretty confident that Opus 4.7 plus Claude Code's built in tool-search-tool would just absorb all of it. it mostly did. but i was still hitting \~20% context saturation way too often, before doing any actual work. tried Ratel (our own MCP gateway, we built it for exactly this problem) kind of out of curiosity. then we benchmarked it properly, head to head against Anthropic's own tool-search-tool, same model (Opus 4.7), realistic tool catalogs at 50 / 100 / 180 tools. at the 180 tool pool, measured against the full-catalog baseline: * Ratel: near parity on accuracy (about -1.7pp) and roughly -81% input tokens. * Anthropic's tool-search-tool: about -8.4pp accuracy. so somewhere around 5x the accuracy hit, same model, same catalog. the takeaway for me: a big context window and a built in tool search are not the same thing as a gateway thats actually optimised for the one job of deciding what enters context. repo plus the full benchmark, numbers and methodology, is here: [github.com/ratel-ai/ratel](http://github.com/ratel-ai/ratel) happy to be wrong on parts of this. if you run it differently and get other numbers id genuinely want to see them.

I need the communities help because I am going around in circles…

**Background:** 1) Deployed a python based, financial pension calculator to Google cloud platform (GCP). 2) Google shell is linked to Claude, making changes to the python scripts that are then pushed to GitHub >>> then to GCP for production 3) I use Claude code locally to troubleshoot what the output from the shell is showing. 4) .md global and local files setup, MCPs setup, hooks, skills and LSP all in place for the project. 5) I have the max plan using Opus 4.7 **Issue**: I am in this loop of copy / paste between local and shell, with no automation. **Ideal outcome:** I want to setup an agent / sub agent environment that monitors and troubleshoots the project, as an orchestrator whilst I focus on developing and enhancing the overall offering and new services. Claude keeps asking for the output from the shell and not automating, it just doesn’t seem to be working! Am I missing something that c laude offers here or is this setup not viable? Am I re-inventing the wheel.. are there clause commands and GitHub repo’s out there that action all this automation so I don’t have to set it up?

This is insane.

Just installed an open source tool that wiped most of the tool-definition tokens out of my Claude Code context before any prompt. Same MCP servers. Same tools available. 8 servers, 142 tools across them. Before: the tool definitions ate 38k tokens of context every single turn. Cold start, my context bar was already orange and I hadn't typed anything. After: 4k. The Claude Code session sees three tools (`search_tools`, `invoke_tool`, `auth`) and dispatches everything else under the hood. When I ask for a thing, it ranks the catalog with BM25 in microseconds and surfaces the top 5. The part nobody's talking about: there's no LLM in the ranking loop. No embedding API to pay. No vector DB to host. It's keyword search over a flat projection of tool name + description, deterministic, offline. Apparently this was always going to be enough. It's [Ratel](https://github.com/ratel-ai/ratel). Open source. The install is `ratel mcp import` and it migrates your existing Claude Code MCP config in one command, with backups written automatically. Took me 90 seconds. Why is every "context layer" startup pitching me semantic embeddings and inference-time re-ranking when basic BM25 over tool definitions does this?

I’m not a developer. I’ve been using codebase memory MCP tools and Obsidian to give Claude persistent memory for my fantasy and sci fi worlds. Here’s what the dev-tool framing completely misses about creative use cases

Hi, I’m an accountant with very little coding experience (took 1 year of CS in college lol) so definitely can’t call myself a developer, but I’ve got a lot of worlds and characters in my head, the need to get them out in writing, and a Claude Pro sub I pulled the trigger on two months ago. I was hoping to see what I could do with things like Claude Code for more non-coding use-cases. So far it’s surpassed everything I’ve experienced except for one, major hang up: **LLM memory for long-context creative writing work still sucks.** Things like brainstorming for a fantasy universe or tracking the game state of a multi-session solo rpg campaign usually starts out pretty well for the first few chats, until you need to mount dozens of lore files and .md style guides to a project, have to wait for it to read all of that, then watch as your session usage bloats out for a simple reply and the quality degradation gets \*really\* noticeable. I’ve been lurking on AI writing subs and the sentiment seems to be shared across the board. So I looked in other places for possible solutions. Then I came across posts in this sub touting Claude memory MCP tools for codebases. Tools like Codesight and MemPalace caught my attention because I thought their applications could extend beyond coding and developer use-cases. The same semantic search and knowledge graph capabilities some of these tools offered for memorizing large, complicated codebases could be used to memorize large, complicated worldbuilding bibles as well, and most of the comments on these posts never mentioned that, or if they did, they were buried or ignored. I decided to test it out myself, starting with MemPalace, a suite of tools that work locally to index your Claude conversations and files into a semantic-searchable knowledge base it can query. My idea started out like this: since I’m already using Obsidian to organize my lore files (with an entry for each character, location, magic system, story arc, etc.) like a wiki or encyclopedia for my worlds, what if I had Claude save my Obsidian vault to its memory so it can recall those lore details whenever the context called for it in any given conversation? I was essentially making a “Second Brain” for Claude out of my Obsidian vault world bible, something I’ve read people doing already but never truly “got” it until I saw it in action. I had no idea about MCP tools before this but before long (and with Claude’s patient help) I was able to wire up the memory palace, mine my obsidian vault info into its memory (organized into verbatim chunks/snippets called “drawers”), and start chatting with it with its new “memories” at its disposal. I was surprised at how seamlessly it worked when I approached this tool sideways. I’d half expected it to work similar to how SillyTavern’s world info and lorebook injection worked, and in fact, I’d been thinking about using these tools to create a similar feature for my own Claude setup, but it was \*not\* like that at all. Lorebook injection worked by listening for a set of keywords that you set up in the World Info tab of SillyTavern, and when one of those keywords is detected in your prompt, it injects the entire lore file from World Info into the chat context. This can cause a lot of token bloat especially if your World Info entries are content-rich or you make a lot of lore references in your chat. What this did instead was make Claude ask plain-language questions to the MCP tools, things like, “What is Gene’s friendship with Felix like?” Or “what is Gene’s relationship to Clara-Belle?” When both of them are in a scene for example. It didn’t just look up Gene and Clara-Belle’s entire lore files and info-dumped everything into context, it pulled up the “Relationships” section of Gene’s file since that’s relevant to the context as well as Clara-Belle’s “Relationships” snippet from her file and any other relevant snippets, then pieced the full picture together through inference. The results: \~2% session usage on a cold start with Sonnet 4.6 with no project or additional context mounted. Claude references character motivations, relationship history, and world/location details I haven’t mentioned in weeks without me prompting it to. It picks up from where we last left off seamlessly across chat after chat. The reconstructive memory aspect I felt works like our own memory and produced perfect recall across sessions. Another side-effect I noticed is that when it references my lore files, it will pick up my style from the way the lore file is written. No more voice-flattening from encyclopedia-sounding lore entries. All the depth, nuance, and psychology I worked hard to cultivate are preserved and the Claude tools are smart enough to factor that in when it replies. I even make sure to add a “Voice” section to each character lore file in that character’s own voice so Claude can pick up on that when it reads that snippet in the tool call and applies it to its current context.

Can Claude code this with one prompt

Can CC clone this landing page with one prompt ? "Visit this page, scroll from top to bottom and clone all the landing page pixel-perfect with same assets. make no mistake [Grand Theft Auto VI - Rockstar Games](https://www.rockstargames.com/VI)"

I didn't want blind multi-agent orchestration or API rates, so I built atrium to keep me in the loop with my CLI agents.

I'd been running multi-agent workflows for a while. Whether it was across multiple projects or on the same project. Brainstorming sessions, planning sessions, builds happening in worktrees, asking for Claude's opinion on new tires for my car cause it was closer to hand than Google. This felt really clunky in most of the tools I was using and when I started looking for alternatives, everything felt like it was trying to remove me from the equation and just run agents in the background. So, I built atrium. A macOS human-in-the-loop multi-agent workspace. The entire project was built with [the BMad Method](https://github.com/bmad-code-org/BMAD-METHOD?tab=readme-ov-file) and Claude Code (mostly Opus). It's over 60 BMad written epics in now and counting. atrium makes CLI agents first-class citizens within a versatile, tiling workspace. It wires up agents via hooks to the app to surface interactive activity cards, saves state comprehensively so everything resumes, provides a robust CLI that allows agents to completely drive the app, and gives me every tool I need to get the job done. Happy to answer any questions about it and would love to hear how y'all are handling multi-agent workflows! If you're interesting in trying it out, it's free on [getatrium.dev](http://getatrium.dev)

Running a website selling agency with Claude doing 80% of the work — what's actually worth adding to my workflow?

Ok so I've been down the rabbit hole for way too long on this and I need actual people who've figured this out to just tell me what works. Basic setup: I run a small agency selling websites to local businesses. Claude handles like 80% of the actual build work, I close the clients and handle the relationship side. It's been working but I know I'm leaving a lot on the table in terms of efficiency and quality. My current process is pretty simple — I create a project in Claude for each client, drop in a [claude.md](http://claude.md), a site\_specs file and a site\_facts file (basically research I've done on the business), and let it cook. Honestly it already does a lot. But here's my problem: I keep running into the same cycle. Basic code errors, obvious visual stuff that I have to manually point out every single time like Claude just... doesn't catch it even when I have error-checking instructions baked in. I fix one thing, something else breaks or it's just a band-aid. It feels like no matter how much I try to tighten things up, there's always friction. I've watched probably too many YouTube videos and read way too many posts but I always end up more confused than when I started because everyone's workflow looks different and half the advice is vague as hell. So what I actually want to know is: \- What specific skills, prompting patterns, or workflow structures have genuinely helped you get more consistent, higher quality output? \- Is there something I'm missing in how I structure my project files that would reduce these recurring errors? \- Any particular review/QA step you've built in that actually catches stuff before you have to? Not looking for "just use a better prompt lol" answers. Looking for people who've actually solved this at a process level. What's working for you?

What was ChatGPT secretly doing on my computer?

No request running overnight, yet 61 Gb, my computer only has 24 RAM, so it probably went digging into the SSD. Should I be concerned? Anyone got that?

If you've ever wondered how rigorous data analysis+social science research can look with AI, I've finally launched a nice website for my open-source Claude Code researcher's toolkit: the Data Analyst Augmentation Framework! Equal parts interactive explainer on agentic orchestration + free tool

If you've ever wondered how rigorous data analysis+social science research can look with AI, I've finally launched a nice website for my open-source Claude Code researcher's toolkit: the Data Analyst Augmentation Framework! Equal parts interactive explainer on agentic orchestration + free tool

Top 10 Fastest Growing AI repos this week

Curated this list of fastest growing AI repos. They are mostly AI coding agents, personal AI, memory, browser automation, Claude Skills and local-first dev tooling: 1. **colbymchenry/codegraph** (+14.1K stars) Pre-indexed local code knowledge graph for Claude Code, Codex, Cursor, OpenCode, and Hermes Agent. 2. **tinyhumansai/openhuman** (+17.1K stars) Personal AI / private AI superintelligence. 3. **Imbad0202/academic-research-skills** (+11.6K stars) Claude Code skills for academic research workflows: research, write, review, revise, finalize. 4. **ruvnet/RuView** (+6.8K stars) Turns commodity WiFi signals into spatial intelligence, presence detection, and vital sign monitoring. 5. **rohitg00/agentmemory** (+6.9K stars) Persistent memory for AI coding agents based on real-world benchmarks. 6. **supertone-inc/supertonic** (+3.6K stars) On-device multilingual TTS running natively via ONNX. 7. **CloakHQ/CloakBrowser** (+7.0K stars) Stealth Chromium that passes bot detection tests with Playwright compatibility. 8. **HKUDS/ViMax** (+2.7K stars) Agentic video generation: director, screenwriter, producer, and video generator in one. 9. **humanlayer/12-factor-agents** (+1.9K stars) Principles for building production-grade LLM-powered software. 10. **Varnan-Tech/OpenDirectory** (+250 stars) AI Agent Skills built for founders who hate marketing. All links in 1st comment 👇

Let the money keep coming in

https://preview.redd.it/q98xb6vqjb3h1.png?width=1080&format=png&auto=webp&s=441dc574c65198e34429d7e410c48c5b6b0ff473 Crazy how we keep on saying AGI is coming soon and a state of art model like opus 4.7 failed at counting number of r's in strawberry. Andrej Karpathy pointed the same problem in one of this interviews where the strawberry fix was hardcoded into LLM instructions. Then came the car wash problem. After this and seeing the claude source code leak and interpreting it. It seems that engineering the LLM properly is what makes it good today. I think we are far from seeing what AGI truly is because there will also be a scenario where human thinking and action is required. Also, since LLM is trained by past events, innovation wouldn't be there at all, if AGI actually works. What's your take on this?

Reconstructing the agent methodology: Decoupling decision-making and execution - open source [P]

I’ve been thinking about a problem in current agent systems: Most agents are becoming very good at execution, but the decision layer before execution is still unclear. Coding agents, research agents, tool loops, sandboxes, workflows, and harnesses are all improving quickly. Once a human gives an intent, agents can often do a lot of useful work. But the higher-level question is still usually left to the user: What should happen next, and why? I’ve been exploring this idea through an open-source project called Spice. The simplest way to describe it is: Spice is a decision layer above agents. It is not trying to replace execution agents. Tools like Claude Code, Codex, Hermes, or other agents can still do the actual work. Instead, Spice sits before execution and tries to make the decision process explicit: - what was observed - what options were considered - why one option was selected - what trade-offs were rejected - whether execution needs approval - what happened afterward - how that outcome should affect the next decision The current runtime is still early, but it can already be installed, configured with an LLM provider, run in the terminal, inspect Decision Cards, and hand off approved execution to external agents. The goal is to make agent behavior less of a black box. Instead of only seeing the final result of an agent task, I want to preserve the reasoning boundary before execution: what the system believed, what it chose, why it chose it, and what changed after the action. GitHub: https://github.com/Dyalwayshappy/Spice I’d love feedback from people building agents. Feel free to fork, star the repo, or share any feedback and ideas. Would love to build this together with the community.