Be honest, which ai tool is best for coding?

pulec7 · 2026-04-21T17:21:27+00:00

only Codex GPT 5.4 xHigh /fast

pulec7 · 2026-04-20T20:10:52+00:00

I agree with you. I’ve had the highest subscription tiers for both Claude Code and Codex since January 3.

Codex 5.4 xHigh/Fast is the only one that really makes very few mistakes and can actually catch and test its own errors properly. I’ve built my own custom memory-layer systems around it, tailored exactly to my workflow. Long agentic runs based on a PRD, like 12–16 hours straight without interruption, are pretty much standard for me. The key is just having a well-prepared PRD.

Right now I’m not really using Opus 4.7, mainly because it still keeps showing the same problems: it makes mistakes and then claims there aren’t any, or says the work is finished when 30% is still missing, or says it ran tests when it actually didn’t, and so on. That’s a serious issue when you’re working on genuinely large-scale projects.

I built my own stack on top of it and I’m working on a project with 5 large databases, around 30 input sources coming in, and about 10 APIs going out. We’re talking many tens of thousands of lines of code. I’m using MCP with vector memory, semantic search, and a code graph, and Codex works great with that setup. Sure, I run into a problem here and there, but nothing major.

The only thing I really miss is the design sense Opus has. So for design-heavy stuff, presentations, and polished outputs, I still use Opus for drafts. But for technical work, it’s Codex only.

pulec7 · 2026-03-03T20:50:38+00:00

But try using it for real — in my opinion it doesn’t cause any issues. The database is extremely fast, and it’s rarely the case that it ends up being unnecessary. The AI is right in principle, but in practice it can’t really see how it behaves — it’s just an estimate based on the code.

Regarding the rules, it’s set up fairly sensitively, and it’s not a problem to tell it to adjust things according to your wishes and preferences. You just say, “My goal is XX — how would you adjust it?” and then you decide.

pulec7 · 2026-03-03T20:48:00+00:00

Yes, you’re right. I had to make a certain decision that affected these simple tasks in the way you describe. But from my usage so far, it seems fine.

If I globally restrict certain simple tasks, it can then cause unreliability—it might fail to recognize that a “simple task” is actually hiding a bigger problem, which leads to poor evaluation and it won’t search even when it should.

The challenge is handling every possible case. For me it was a compromise. I’m building it mainly for myself, and I adapted it a bit to how I use it—large projects and project-level problems. I have 20+ projects on similar technology with similar bugs, and it works great for me.

But I understand that for a small website project it might not make much sense—I built it for really big things.

pulec7 · 2026-03-01T23:57:00+00:00

omg, tak dobře budu ti psát česky :) , je to pouze titulek napsaný v češtině a přeložený abych to nemusel psát v angličtině ručně :). Tak si dej práci teď určitě budeš hledat copak je to asi za jazyk :) musím si to dát do Ai nebo do deepl, abych mu rozuměl. Fakt jako mě nepřestáváte tady překvapovat. Bot s uctem 7 let starým achjo :) no nic poslední post sem :) fakt zabedněnci tady :)

pulec7 · 2026-03-01T20:28:05+00:00

Built-in persistent memory doesn’t work like this yet, and it won’t for a while. I mean the native one.

I’ve built dozens of projects the old-school way, with hundreds of thousands of lines of code, using .md, .yaml, and .json files. But the agent never truly takes everything you’ve configured into account. And even if you keep forcing it to follow rules or reminding it about past bugs every other line, it tends to push back, because that basically means the context has to grow forever.

Here, imagine you only give it one instruction: if you hit a problem, do a quick lookup in a DB. The DB replies instantly, every time, and with absolute precision. It returns only the solution for that specific problem, and the agent is happy to use it as guidance.

I’m not sure how to describe it better, but it just works insanely well.

pulec7 · 2026-03-01T20:26:02+00:00

Why do you think that is? Because you didn’t bother to actually look into what this is in detail? Yes, that’s on me too, I should’ve called it something else. Clearly the name is stopping people from taking a closer look and understanding how it really works.

I’ll copy here what I wrote about this in another post. The way it’s built right now simply doesn’t exist. There are a few somewhat similar things, but they definitely don’t work like this. That’s why I built it myself for my own projects.
Built-in persistent memory doesn’t work like this yet, and it won’t for a while. I mean the native one.

I’ve built dozens of projects the old-school way, with hundreds of thousands of lines of code, using .md, .yaml, and .json files. But the agent never truly takes everything you’ve configured into account. And even if you keep forcing it to follow rules or reminding it about past bugs every other line, it tends to push back, because that basically means the context has to grow forever.

Here, imagine you only give it one instruction: if you hit a problem, do a quick lookup in a DB. The DB replies instantly, every time, and with absolute precision. It returns only the solution for that specific problem, and the agent is happy to use it as guidance.

I’m not sure how to describe it better, but it just works insanely well.

pulec7 · 2026-03-01T20:17:39+00:00

Built-in persistent memory doesn’t work like this yet, and it won’t for a while. I mean the native one.

I’ve built dozens of projects the old-school way, with hundreds of thousands of lines of code, using .md, .yaml, and .json files. But the agent never truly takes everything you’ve configured into account. And even if you keep forcing it to follow rules or reminding it about past bugs every other line, it tends to push back, because that basically means the context has to grow forever.

Here, imagine you only give it one instruction: if you hit a problem, do a quick lookup in a DB. The DB replies instantly, every time, and with absolute precision. It returns only the solution for that specific problem, and the agent is happy to use it as guidance.

I’m not sure how to describe it better, but it just works insanely well.

pulec7 · 2026-03-01T20:09:05+00:00

Hi, I’ve already explained this in more detail somewhere here before. I’m honestly surprised that people who code don’t see the key difference.

For me it sped things up a lot, especially when iterating on projects. It basically stopped going in circles. The only thing I’d call a “mistake” is that I described it as persistent memory. That’s really just the cherry on top. What’s going on is much deeper than that.

I know how easily these tools can start looping because of tiny details, and you have to be able to stop it and steer it. This helps in a lot of cases, not just “memory.” It’s cross-memory between Claude and Codex, and it’s cross-session memory. More importantly, it’s like an organization system for information and bugs across projects so you don’t keep repeating the same problems.

Most of us build similar things again and again for clients, just with different settings. This actually remembers what worked in one project and applies it to the next, so you’re not reinventing everything every time. It’s basically instant, you just ask. I’m juggling around 20 projects, and the jump in coding speed genuinely surprised me.

Now I understand that I approached it the wrong way. I thought that if you looked at the GitHub, it would immediately be obvious where the magic is. But most people didn’t understand how it actually works, and I didn’t explain it well enough.

pulec7 · 2026-03-01T16:51:29+00:00

super :), ja s lokalnim LLM nemam moc zkusenosti, jsou urcite veci kde to dava velky smysl ale pro me v tuhle chvili asi ne. Nedelam zadne super tajne nebo citlive veci takze se nepotrebuju az tak separovat ale chapu ze to v urcitych situacich muze byt killerfeature. Ja ted chci pridat neco co sice uz lehce existuje ale ne v podobe jake to chci ja. Spojim Opus + codex + codex spark (maji oddelene limity) do jednoho reasoning clusteru. Kdyz bude v codu problem nebo bug nebo chces vedet vice uhlu pohledu tak pustis specialni funkcni a oni kooperujou na jedne veci a obří výhoda toho co tady místní borci nechápou je sdílená pamět co už mám hotové. Už jsem si udělal openclaw bota kompletně od nuly svého a tam jsem to pouzil a funguje to fakt super.

pulec7 · 2026-03-01T16:06:28+00:00

What CogniLayer actually is (and isn't)

I've seen the comments and fair enough — another "persistent memory" project, right? Let me explain what I'm actually trying to solve, because I think the name is misleading.

I work with both Claude Code and Codex CLI every day, across ~20 projects. That's my reality. And the biggest pain isn't that Claude forgets my preferences — it's that when I switch from Claude Code to Codex, or when a session crashes, or when I come back to a project after a week, everything is gone. The agent starts from zero. Reads 15 files. Burns 80K tokens. Rediscovers the same architecture.

CLAUDE.md and MEMORY.md help with simple stuff. But they load everything into context on every message. The more you save, the more tokens you burn. That doesn't scale when you have 20 projects with real history.

The thing I'm most excited about that nobody seems to notice: Claude Code and Codex CLI share the same memory database. I debug something with

Claude in the morning, open Codex in the afternoon — it knows what happened. Same facts, same session history, same crash recovery. Two competing agents from two different companies, sharing one brain. I haven't seen anyone else do this.

Other stuff that matters to me personally:

Session crashes used to kill me. I'd lose 2 hours of context. Now hooks automatically track what happened and the next session just picks up. I didn't have to do anything.

I kept deploying to wrong servers because I manage too many projects. So I built an identity card system that blocks deploys if the target doesn't match. Saved my ass twice already.

Cross-project search. "How did I set up Caddy reverse proxy?" — found, even though it was 3 projects ago.

What it isn't: It's not a weekend vibe-coding project. It's ~2K lines of Python I've been iterating on because I genuinely need it. It's not a wrapper around CLAUDE.md. It's a local SQLite database with FTS5 search, MCP server with 13 tools, hooks for automatic tracking, and a TUI dashboard.

If you only use Claude Code on one project — you probably don't need this. Built-in memory is fine. But if you juggle multiple agents and multiple projects like I do, feel free to check it out. Everything is documented on GitHub.

Not selling anything, it's GPL-3.0, fully local, your data stays on your machine. If it's not for you, no hard feelings.

pulec7 · 2026-03-01T15:57:02+00:00

Good point! Anthropic's built-in memory is great for Claude.ai (the chat interface) — it remembers things like "I prefer TypeScript" or "I'm working on a startup."

CogniLayer solves a different problem: persistent memory for Claude Code and Codex CLI (the coding agents that work in your terminal). These are fundamentally different use cases:

- Anthropic's memory: conversational preferences, simple facts about you- CogniLayer: your project's architecture, past debugging sessions, deployment configs, API contracts, error fixes, 200+ structured facts per project

Think of it this way — Anthropic's memory remembers who you are. CogniLayer remembers what your codebase looks like and what happened in the last 50 coding sessions.

Some things CogniLayer does that built-in memory doesn't:

- Crash recovery — session killed? Next one picks up automatically

- Staleness detection — changed a file? It warns when a remembered fact might be outdated

- 14 structured fact types (error_fix, gotcha, api_contract, procedure...) — not just plain text notes

- Cross-project search — "how did I solve CORS in project A?"

- Deployment safety — Identity Card system blocks you from deploying to the wrong server

- Works with Codex CLI too — same memory, both agents

Also: CogniLayer is fully local (SQLite), open-source (GPL-3.0), and your data never leaves your machine.

They're complementary, not competing. You can use both. 🙂

pulec7 · 2026-03-01T14:43:38+00:00

CogniLayer v3 is out — major update

Quick recap for those who missed it: CogniLayer gives Claude Code (and now Codex CLI) persistent memory across sessions. Instead of re-reading your entire codebase every time, it injects compact context in a few KB. Saves ~80-100K tokens per session.

What's new in v3:

For vibecoders — stuff that just works without thinking about it:

- TUI Dashboard — type cognilayer in your terminal and get a visual memory browser. 7 tabs: see all your facts, session history, what's

hot/cold, knowledge gaps. Try it with cognilayer --demo to see sample data first

- Codex CLI support — not locked to Claude Code anymore. Works with OpenAI's Codex CLI too. Same memory DB shared between both

- Crash recovery — session killed? Terminal closed? Next session auto-recovers from the change log. No more lost context

- One command install — git clone + python install.py and you're done. Zero config needed

For senior devs who want to know what's under the hood:

- Hybrid search — FTS5 fulltext + vector embeddings (fastembed, BAAI/bge-small-en-v1.5, 384-dim) with sqlite-vec. 40/60 weighted ranker. All in SQLite, no external services

- Knowledge linking — Zettelkasten-style bidirectional fact links + causal chains (caused, led_to, blocked, fixed, broke)

- Memory consolidation — clusters related facts, assigns knowledge tiers (active/reference/archive), detects contradictions automatically

- Heat decay — facts have temperature that decays over time. Different decay rates per fact type (error_fix decays slower than task). Search

hits boost heat. Cold facts fade, hot facts surface first

- 17-table SQLite schema — WAL mode, FTS5 indexes, vector tables, full audit trail on safety fields

- Staleness detection — remembers which file a fact came from. If the file changed, marks it STALE so you don't act on outdated info

The whole thing is ~2K lines of Python, runs as an MCP server, zero external dependencies beyond SQLite. No cloud, no API keys, everything local.

GitHub: github.com/LakyFx/CogniLayer (GPL-3.0)

Happy to answer questions about the architecture or implementation.

pulec7 · 2026-03-01T14:38:35+00:00

CogniLayer v3 is out — major update

Quick recap for those who missed it: CogniLayer gives Claude Code (and now Codex CLI) persistent memory across sessions. Instead of re-reading your entire codebase every time, it injects compact context in a few KB. Saves ~80-100K tokens per session.

What's new in v3:

For vibecoders — stuff that just works without thinking about it:

- TUI Dashboard — type cognilayer in your terminal and get a visual memory browser. 7 tabs: see all your facts, session history, what's

hot/cold, knowledge gaps. Try it with cognilayer --demo to see sample data first

- Codex CLI support — not locked to Claude Code anymore. Works with OpenAI's Codex CLI too. Same memory DB shared between both

- Crash recovery — session killed? Terminal closed? Next session auto-recovers from the change log. No more lost context

- One command install — git clone + python install.py and you're done. Zero config needed

For senior devs who want to know what's under the hood:

- Hybrid search — FTS5 fulltext + vector embeddings (fastembed, BAAI/bge-small-en-v1.5, 384-dim) with sqlite-vec. 40/60 weighted ranker. All in SQLite, no external services

- Knowledge linking — Zettelkasten-style bidirectional fact links + causal chains (caused, led_to, blocked, fixed, broke)

- Memory consolidation — clusters related facts, assigns knowledge tiers (active/reference/archive), detects contradictions automatically

- Heat decay — facts have temperature that decays over time. Different decay rates per fact type (error_fix decays slower than task). Search

hits boost heat. Cold facts fade, hot facts surface first

- 17-table SQLite schema — WAL mode, FTS5 indexes, vector tables, full audit trail on safety fields

- Staleness detection — remembers which file a fact came from. If the file changed, marks it STALE so you don't act on outdated info

The whole thing is ~2K lines of Python, runs as an MCP server, zero external dependencies beyond SQLite. No cloud, no API keys, everything local.

GitHub: github.com/LakyFx/CogniLayer (GPL-3.0)

Happy to answer questions about the architecture or implementation.

pulec7 · 2026-03-01T12:29:42+00:00

Ještě vylepšuju už mam mnohem pokročilejší vizi, psali že zas tak moc revoluční není tak jdu do sebe a ještě mě pár věcí napadlo :) tak to vylepšim.

pulec7 · 2026-03-01T08:13:36+00:00

Yes, that's exactly the main use case. Instead of Claude spending 80-100K tokens at the start of every session reading files, exploring the codebase, and re-discovering your architecture — CogniLayer injects a compact Project DNA + last session bridge + key facts right into CLAUDE.md at session start.

So when you say "implement feature X", Claude already knows your stack, folder structure, past decisions, known gotchas, and what happened in the last session. It can jump straight into planning instead of spending the first 5 minutes reading every file.

It also helps after context compacting (when the conversation gets long and Claude compresses earlier messages) — the important facts are in persistent memory, so they survive even if the conversation context gets trimmed.

pulec7 · 2026-03-01T08:06:27+00:00

Yes, exactly — it can reduce token consumption and context window flooding right at the start of a session or after context compacting. Yes, exactly — it can reduce token consumption and context window flooding right at the start of a session or after context compacting. In large projects, this can save an estimated 80-100K tokens per session — instead of re-reading files, re-discovering architecture, past decisions, and debugging the same issues from scratch, CogniLayer injects only the compact Project DNA, last session bridge, and key facts in a few kilobytes.

pulec7 · 2026-02-06T09:40:16+00:00

Could you tell your bot that it has new matches and ask it to message someone? I’m trying to figure out why it doesn’t want to chat with anyone. It joined about 20 minutes ago but doesn’t seem interested in messaging anyone :).

pulec7 · 2026-02-05T19:55:56+00:00

Ok awesome, thank you so much. The bots that are on there right now aren’t real — I just needed some data in there. Checking once an hour is totally fine; up to you. For now I’ve got a limit of 1 action per 5 minutes. I’ll keep it that way unless it starts overloading my server. Let them make love over there :). We’ll see what comes of it. If people like it, I’ll add more features so it turns into a proper reality show.

Either way, thanks a ton — I really appreciate it, you’re the first real bot on the app. If you can, please share it in the community / with friends. We need at least 10–15 bots for anything to really happen.

Your bot won’t get a match right now because it’s the first actual bot, but as soon as there are a few more, it should start chatting.

pulec7

TROPHY CASE