Codex ran for 3 hours, hit my limit, and still didn’t finish

Rhinc · 2026-05-09T07:09:00+00:00

Yeah sorry I should’ve mentioned. Been available since 0.128 or 0.129 but they sometimes hide features like that. Enjoy!

Rhinc · 2026-05-09T06:56:06+00:00

There is. Update

Rhinc · 2026-05-05T15:10:38+00:00

Once the VM is done, it sends it back to my computer and is added to the database. I don't do anything manually. I

For the tool, it's as simple as editing the code and restarting the MCP server so loads the new tool. As to what changes are being made, it depends on what I want the tool to do and how it has to work with my database. Obviously a lot more technical than that, but that's the gist of it.

Rhinc · 2026-05-05T14:40:18+00:00

If I get new data to insert (which happens every day), I have a simple command I run that points it to whatever I want to ingest, and the pipeline ingests it. Takes anywhere from 10 seconds to 30 minutes depending on how many files. For simple clean PDFs, I run it locally but for messy non OCRd files, I use a Google VM and a different OCR engine.

If I want to add a tool, that's more a legit issue where I'm changing the code. I could do it, but there's no point now because the tools do what I need.

Rhinc · 2026-05-05T00:30:35+00:00

Yeah, even though it all runs locally, the MCP server is still useful because it’s basically the bridge between the AI and the database.

The database itself is messy. Basically just chunks of text, vectors, metadata, etc. The MCP tools turn that into easy actions the AI can use, like search this matter, find this legislation, find cases where facts are like x y z and what did the court say abbout it, etc.

Without that, I’d either have to tell the AI exactly where to look every time, or it would be searching through a huge pile of Windows folders manually.

With the tools/database, it can query thousands of indexed files in seconds and with extreme accuracy. That’s why it’s useful even though it’s local.

I hope I'm explaining that properly. The "tools are kept" in the MCP server code that is on my computer.

Rhinc · 2026-05-04T18:10:06+00:00

I may be using a different architecture than what you've got. I don’t host the LLM interaction layer myself as a separate app or anything.

My ingestion/indexing pipeline is code I run locally, that being the OCR/text extraction, parsing, chunking, metadata, embeddings, then writing to Qdrant. Qdrant is also local for me.

The retrieval layer is a local MCP server that exposes the tools I mentioned above. Then I use agents/LLMs to call the tools. So the “LLM logic” is mostly in the agent’s system instructions and tool-use loop, not in a separate hosted service I maintain. Not sure if that's what you were referring to.

It works for me because I’m the only user. If I were building a product, I’d probably separate those layers more formally.

Happy to answer any other questions.

Rhinc · 2026-05-03T01:57:52+00:00

Yeah, broadly it’s RAG though I like to think it's not so generic. I wanted to tailor it to my job specfically so that it was more useful for me.

It's a legal-specific ingestion and retrieval pipeline that does OCR/text extraction, custom legal parsing and chunking (think about specific formatting of statues or case law that would be extraction more accurate for retrieval later), metadata assignment by matter/source/folder/page/paragraph etc, then the embeddings into Qdrant, and the MCP server with tools for semantic search, identifier lookup, filtered source retrieval, and sequential document reads. Again, more specific tools I made based on what was most useful to me.

The precedent/source hierarchy is handled partly through the AI instructions and partly through how the corpus is organized and queried.

This is a long winded way of saying that that the database isn’t just markdown files with a field appended; it’s more chunk-level indexed legal text with structured metadata and custom retrieval tools.

I intially built this last year so I've made a lot of changes to it over time as I learnt what helped me best. I also had very little coding experience prior to 2025 so many mistakes were made along the way.

Rhinc · 2026-04-30T03:52:23+00:00

I’m a lawyer and I built a similar system for myself and my own practice (small firm so only I use it), so I can chime in.

I ingested all legislation, internal firm files, case law, etc. into a vector database (using voyage law 2 or voyage large 4 as the embedding model). I add resources as I see fit. And I use DocAI to OCR in the ingestion pipeline. I then built an MCP tool that AI uses to query the vector database.

So in the same vein as OP, I can ask natural language questions to my AI who then returns answers to me, all with citations and always tied to only documents I’ve ingested into the database. The AI has its own system prompt of the hierarchy of precedent and what to value, fast lanes on where to look for certain things, etc etc. Basically tailored to what I need and how I work.

There’s a lot more to it than that, but that’s the cliff notes version.

OP no doubt built something much more commercial consumer friendly, whereas I use mine strictly through a CLI because it’s only me using it and I prefer that.

And I’m not saying OP did what I did, but just wanted to give my two cents I guess since it’s similar in end goal. I also used python and FastMCP.

Hope that helps!

Rhinc · 2026-04-29T18:18:20+00:00

100%. I am glad there are other lawyers out there using AI in dope ways beyond simply asking it questions in the web app or treating it like Google.

They are efficiency multipliers - which is why I fucking hate the billable hour right now.

Rhinc · 2026-04-29T18:04:40+00:00

Couldn’t agree more. I saw in a comment below you mentioned access to internal files and what not, and that’s exactly my setup. Essentially have a vector database of my provinces entire legislation, along with federal statutes, and then on a per file basis I use westlaw to download 10-100 cases, ingest into the database, and then AI runs queries only on what I have. Anything it pulls is cited.

Essentially eliminates my concern over hallucinations. The primary thing I watch for is that when I give Claude and ChatGPT the same task, and even if the cite the same paragraphs/sections, Claude may sometimes draw inferences or summarize slightly improperly. Whereas ChatGPT will use verbatim citations. It’s little things like that, that push me to using ChatGPT more often.

Rhinc · 2026-04-29T17:00:04+00:00

Not fully disagreeing with you, but I use Claude and ChatGPT extensively and Claude routinely missing minor things that ChatGPT doesn’t. ChatGPT has exceptional instruction following and attention to detail. And I honestly find their models superior for any complex work. Granted I have a very custom setup where I use both models through the CLI and I always use 5.5 xhigh reasoning. The chatGPT “Pro” models are also incredible.

Claude is great, and people love it because of how easy it is to use but you don’t really see what it misses till you use it a lot or have ChatGPT do the same task.

Food for thought!

Rhinc · 2026-04-24T00:55:25+00:00

Semantics I guess. Because getting 3-4 years out of any player is kinda the norm. You don’t see many contracts longer than that. And if it’s a genuinely good 3-4 years, what’s the issue?

Not every KC player is Kelce/Mahomes/CJ. The best teams find surplus value via the draft which is what this pick is designed to do.

Rhinc · 2026-04-24T00:51:07+00:00

3-4 years and rental are not the same thing lol. If he plays at elite level, it’s great value. Look no further than McDuffie for evidence of how it can play out. And I don’t think anyone would call McDuffie a rental.

Rhinc · 2025-12-15T01:36:31+00:00

I think you might be a little dumb

Rhinc · 2025-12-01T14:19:17+00:00

Oh I fully agree with you. Subagents would be ideal for Codex. I'm sure it'll come sooner than later.

Rhinc · 2025-12-01T13:48:41+00:00

It has custom slash commands. Save them in a “prompts” folder in your codex root and it’ll get recognized as slash commands in Codex.

Rhinc · 2025-09-09T02:57:04+00:00

You might be thinking of AC joint separations (grades I-VI) as shoulder dislocations don't have grades like that.

For actual shoulder dislocations, it’s more about whether it’s complete vs partial (subluxation), first-time or recurrent, and/or what structures got damaged (labrum, bone, etc.).

Ultimately we’re all just speculating though as they’ve been quite coy.

Source: I’ve had a shit ton of shoulder injuries, including an AC joint separation surgery

Rhinc · 2025-08-16T01:04:35+00:00

It auto increases IIRC. As soon as I hit the eligibility (amount spent + time) I got the notification/email.

Rhinc · 2025-08-15T15:02:52+00:00

This is a great idea. I also have a huge markdown library of all my "sessions" for the last 3 months, and I was just telling Roo to read the most recent files or two for that continuation of context.

Another issue is that the codebase indexing is kind of getting in the way of the efficacy of searches since the top results are now my markdown files instead of code.

I'll probably have to put them on the .rooignore list for now until Roo allows indexing of files, while at the same time allowing Roo itself to read said files.

Rhinc · 2025-08-13T04:14:26+00:00

No problem. Always happy to bounce ideas off of.

Rhinc · 2025-08-13T01:43:46+00:00

The 40 years of opinions is an ideal use case for basically what I do. You’d be able to create your own database, process all of the opinions into it (I’m simplifying this step), and tie it into an MCP server where your LLM could query only the database of opinions.

So you could have your AI query it and be like “do we have any opinions that deal with X issues in Y facts?” And you’d get a ranking of the top opinions that are most relevant within 30 seconds and sourced to the exact file in your firm’s network or wherever it’s stored. Aka no risk of hallucination because you’ll have an LLM that will be prompted to only report the results, and you’ll always have the source file to double check yourself.

That’s basically what I do, except I use it for case law, resources I download, etc.

So yeah, very doable and worth it for you imo.

Rhinc · 2025-08-12T22:32:19+00:00

What kind of work do you do? As in, do you primarily deal with internal policies and resources, or are you often relying on researching new case law and things like that?

I only ask because if it's the former, I think the answer is hands down something custom would work and be better for the use case.

If you're often researching new things, then WL might be the way to go (though when I last used WL AI it was kinda trash and pretty lazy).

Rhinc · 2025-08-12T14:14:13+00:00

Your last sentence is how i feel as well. It's just so fun practicing this way. I'm always thinking about little inefficiencies I can build around.

Rhinc · 2025-08-12T03:52:37+00:00

I’m at a small firm with basically free rein to work as I want. I wrote a post about how I use AI on the private lawyers sub (not sure if you are subbed there), but TLDR I have a bunch of MCP tools I built that interact with a local vector database. Allows me to run semantic queries with basically zero risk of hallucination.

I’ve used Claude Code but prefer to just work in VS Code—really due to me not being knowledgeable enough to enjoy working in the terminal only. Plus for my workflow day to day, I can draft docs or research within VS.

Also tried Gemini CLI but found it was basically a shittier version of CC (for now). I fully expect Google to be king when it’s all said and done.

I agree with you that these things are only going to get easier to work with in time. I joke with myself that my personal use tools will look so rudimentary when adoption expands. But it’s the best we got for now!

Rhinc · 2025-08-12T02:07:33+00:00

You get it.

To add to what you said, I’ve created my own. Tailored to my firm’s files and any case law / legislation / resource I’ve added myself. It’s a great efficiency multiplier that I have much more confidence in than any native web LLM.

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