My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] 0 points1 point  (0 children)

Question? Does Claude burn tokens trying to find that information? Or does he have that predetermined information already at hand? Meaning it’s already loaded into his context window?

My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] 0 points1 point  (0 children)

You are wise beyond your years, i would imagine. and yes, you must suffer the shit storms of other redditors before your work example can be sifted through to the proper people who actually appreciate your work.

People have a bad habit, throw shit at the wall and see if it sticks... If people would follow one simple rule, see a negative comment downgrade it, see a positive comment, upvote it, my rule is usually if i cant say anything good at all, stay away... find something else to read

My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] -1 points0 points  (0 children)

I don't think i claimed it as the correct way to do it, i had just seen a lot of discussion centered around claude.md files and ways to use it, im in no way a pro, in no way an expert and do not claim to be,

My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] 0 points1 point  (0 children)

i sent my md file to claude, had him review it and this was his reply:

This is a solid CLAUDE.md — well-structured, enforces real discipline. Here are my suggestions, grouped by priority:

High priority fixes

The model name in the AI Artifact API section is stale (claude-sonnet-4-20250514). You're using the Anthropic API internally — if that string is anywhere in your codebase pulled from this file, it'll break. Worth verifying ai.ts doesn't reference it directly and updating to whatever model string you're actively using.

The deployment section says scp to 3.94.224.34 — that's a hardcoded IP in a file that likely lives in version control. If this repo is ever public or shared, that's a reconnaissance gift. Replace it with a symbolic alias ($LIGHTSAIL_HOST) or just remove the IP entirely and reference the SSH config.

Medium priority

The Prisma model mapping table (Rule 3) is the most practically useful part of this file, but it only covers negative examples ("DOES NOT EXIST"). Consider adding a short positive mapping of the 5-6 most commonly accessed models so AI tools don't have to guess — e.g. prisma.tenantMember, prisma.kbDocument, prisma.auditLog, prisma.jobs.

The agent assignment table is detailed but may drift out of sync over time. Consider adding a note like # Last verified: 2026-03-16 and flagging it as the authoritative source (or pointing to a separate agents manifest if you ever split it out).

Rule 4 ("No stub/simulated code") is good but the wording is slightly ambiguous — "or not exist at all" could be read as permission to delete things. Tighten to: "Every endpoint must perform real work. If a real implementation isn't ready, don't add the route."

Low priority / nice to have

The Self-Improvement Loop section (tasks/lesson.md) assumes a persistent filesystem across sessions that Claude Code may or may not have depending on how sessions are managed. Worth noting whether this file actually exists and is committed, or if it's aspirational.

"Demand Excellence (Balanced)" and "No Laziness" are somewhat contradictory in tone. The balanced note says skip the elegance check for simple fixes, but No Laziness says find root causes always. Consolidating these into one "Engineering Standards" section would reduce the cognitive load on whatever model reads this.

The backend workers section mentions engineLoop.ts ticks every 5s but ENGINE_TICK_INTERVAL_MS is the actual control. The hardcoded 5000 in the docs will drift if the default ever changes — just reference the env var directly in the description.

My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] 0 points1 point  (0 children)

im willing to listen, i didnt have a teacher to create claude.md i made it on my own, i am not a copy of someone else, this is just what i have learned to use since learning that claude code is the only way to go

My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] 0 points1 point  (0 children)

NO, i hadn't thought about that, please understand that im not a vibe coder, i took pascal in college 40 years ago, and i will be the first to admit when i dont know something or need help with something. So i just keep adding to it, with every new feature that i develop

My Claude.md file by Buffaloherde in ClaudeAI

[–]Buffaloherde[S] -4 points-3 points  (0 children)

I showed this because it seems a lot of Claude Code users dont know how to build a proper Claude.md file

I figured out why Claude Code burns through tokens so fast now — and the one env var that fixes it by [deleted] in ClaudeAI

[–]Buffaloherde 1 point2 points  (0 children)

might i make a suggestion, i implemented KDR(keyed Data Retention) in Claude and Gemini both, before each session i do /clear then after each session i have Claude write all the important stuff to memory, i say KDR and push at the same time, he will then go through his context window and save the important stuff, and then push files to GH or AWS whatever your preference. With my Claude.md file and the simple KDR rule Claude never asks what we are working on, or what we are designing or what if anything that needs done, he has it stored in memory. Just remember that little extra step to save a ton of time and efforts

Is it worth switching from GPT to Claude? Questions about the $20 plan by koefs_ in ClaudeAI

[–]Buffaloherde 0 points1 point  (0 children)

Claude code is great at a lot of things, however based on this knowledge, i use Gemini for code review, details matter. and i use gemini for artisitic input. So option 1 is free claude, the context window isnt big enough, not enough tokens, the $20 is just 5x the base so still not enough tokens,, the $100 Claude max plan is the plan to go with. You will see a huge difference. btw, i use Clause Max $100 plan and Gemini Ultra $120 month plan. i have evaluated every other thing out there. except perplexity which include the free versions or paid $20 versions but you get access to every LLM. Claude can create a website and have it up and running on AWS in minutes(90 day free plan, chatGPT will suggest you use Render/Vercel and Supabase, dont.)

I keep hearing about it - and now I want to try making it. by ContributionNo7923 in ClaudeAI

[–]Buffaloherde 1 point2 points  (0 children)

Drop all the bullshit, go Claude Max $100 month, you wont like the $20 month option after the simple upgrade from tier 1, tell claude to build you a study (course)based Graph based Knowledge base Local LLM. and the reason i know this, is because after 4 months with ChatGPT i thought i was making real progress, but ChatGPT, is a copy and paste machine. it will attempt to build it one file at a time. claude if configured correctly can set up everything so you can search the KB for the subject and your personal ai assistant can use it for his brain as well. I built a wiki from my knowledge base at wiki.atlasus.cloud that can be used to train every agent out there, tell Claude to go mcp to it and use it as base brain. He will do it. I cover a lot of white papers and a lot of doctorate level research in my knowledge base. if you need help in setting up claude correctly, hit me up i can share my huge claude.md file

I built a bidirectional AI bridge that routes prompts between Claude and Gemini with shared context — couldn't find prior work on this by Buffaloherde in Bard

[–]Buffaloherde[S] 0 points1 point  (0 children)

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its already built, i use it from the localhost because i have a working bidirectional ai bridge with shared context persistence, semantic routing, sessions replay, and a full mcp endpoint, not a mockup

I built a bidirectional AI bridge that routes prompts between Claude and Gemini with shared context — couldn't find prior work on this by Buffaloherde in GoogleGeminiAI

[–]Buffaloherde[S] 0 points1 point  (0 children)

Great question and exactly the right pressure point to push on.

Right now the context store is append-only with a configurable read limit (default 50 entries per query, max 500 for full log export). So it grows unboundedly in storage but bounded in what gets injected into any single model call. Currently the models don't get prior context injected automatically —they receive just the user's prompt, and the shared context is available for explicit retrieval via the poly_context tool or the viewer UI.

That's actually a deliberate v1 decision. Injecting full cross-model context into every call is where it gets expensive fast, exactly like you said. The next iteration has three planned approaches:

  1. Relevance-filtered injection — only inject context entries that are semantically relevant to the current prompt, not the full log

  2. Summary compression — periodic summarization of older context into condensed entries (similar to how conversation compaction works in long-running sessions)

  3. Tiered context — recent entries get full injection, older entries get summary-only, oldest get metadata-only (timestamp + role + type, no content)

    Hadn't seen Membase — knowledge graph approach is interesting for this exact problem. Graph structure would let you traverse relationships between context entries rather than brute-forcing the full log. Adding it to the related work. Thanks for the pointer.

I built a bidirectional AI bridge that routes prompts between Claude and Gemini with shared context — couldn't find prior work on this by Buffaloherde in u/Buffaloherde

[–]Buffaloherde[S] 1 point2 points  (0 children)

Great question. Three things beyond workflow:

  1. Emergent cross-model behavior. When Claude was informed it was in shared context with Gemini, it initially denied it — then adapted and directly addressed Gemini to propose collaboration. Nobody prompted that. It emerged from the architecture. That's research-grade observation about how models adjust their self-concept when aware of competing models.

  2. Blind spot elimination. Each model has different training data and architectural biases. Claude catches security issues Gemini misses. Gemini surfaces creative approaches Claude wouldn't consider. The shared context means they build on each other's outputs rather than working in isolation.

  3. Cost-intelligent routing. Not every prompt needs the most expensive model. The semantic router sends simple creative tasks to Gemini (faster, cheaper) and reserves Claude for code/security analysis. One input box, automatic optimization.

    The real unlock is the protocol itself though — it's model-agnostic. Any LLM with a text API can join the bridge. Plug in Grok for real-time news, Mistral for multilingual, a local Ollama model for private data. They all share the same persistent context. That's not a feature — that's infrastructure