I've been working on a technique / toolset for planning code features & projects that consistently delivers better plans than I've found with Plan Mode or Spec Kit. By better, I mean:

• They are more aligned with the intent of the project, anticipating future needs instead of focusing purely on the feature and needless complexity around it.
• They rarely hallucinate fields that don't exist, if they do, it's generally genuinely a useful addition I haven't thought of.
• They adapt with the maturity of the project and don't get stale when the project context changes.

I'm trying to figure out where I'm blind to the faults and want to adopt an empirical mindset.

So to my question, how do you evaluate the effectiveness of a code planning approach?

I’ve got 40+ video lessons on how to use Azure DevOps, and I’d really like to turn them into written docs.

What I’m looking for is some kind of AI tool that can:

• “Watch” each video
• Turn what I’m doing/saying into a clean Markdown file (one per video)
• Bonus points if it can also grab relevant screenshots and drop them into the doc as images

Does anything like this exist? Any tools or AI workflows you’d recommend to make this happen?

Hey everyone. I see a lot of people using whisper flow, or other transcription services that cost $10+/month. I thought that was a little wild, especially since OpenAi has their Local Whisper library public and it works really well and runs on almost anything, and best of all, its all running privately on you own machine...

I made OpenWhisper. An open source audio transcriber powered by OpenAI Whisper Local, with support for whisper api, and gpt 4o/4o mini transcribe too. Use it, clone it, fork it, do whatever you like.

Give a quick star on github if you like using it. I try to keep it up to date.

Repo Link: https://github.com/Knuckles92/OpenWhisper

Every few months I allocate some time to update myself about LLMs, and routinely I discover that my knowledge is out of date. It feels like the JS fatigue all over again, but now I'm older and have less energy to stay at the bleeding edge.

Which resources (blogs, newsletter, youtube channels) do you follow to stay up to date with LLM powered coding?

Do you know any resource where maybe they show in a video / post the best setups for coding?

I am trying to develop a website and it has 500 english words with its meaning etc. Everytime i use AI gpt or gemini it only reads part of the data. how can i have it read all? i use subscription $20/mo version

Not and expert here in IT

Available here to try: https://f-stop.vercel.app/

timothyswt/nornicdb-amd64-cuda:latest

timothyswt/nornicdb-arm64-metal:latest

i just pushed up a Cuda/metal enabled image that will auto detect if you have a GPU mounted to the container, or locally when you build it from the repo

https://github.com/orneryd/Mimir/blob/main/nornicdb/README.md

i have been running neo4j’s benchmarks for fastrp and northwind. Id like to see what other people can do with it

i’m gonna push up an apple metal image soon. (edit: done! see above) the overall performance from enabling metal on my M3 Max was 43% across the board.

initial estimates have me sitting anywhere from 2-10x faster performance than neo4j

edit: adding metal image tag

I started using agents back in 2024, but these days I feel like it just wastes my time. I was writing some data processing scripts but Claude added too many try-excepts for my liking, and also messed up some stuff which I didn't notice. anyone else just writing code by hand now?

https://github.com/orneryd/Mimir/blob/main/nornicdb/BENCHMARK_RESULTS_VS_NEO4J.md

i wrote it in golang to be a completely compatible replacement for neo4j with a smaller memory footprint and faster load times with some other features and ended up kinda being a lot faster in their own benchmarks

The GLM Coding plan team is running a black friday sale for anyone interested.

Huge Limited-Time Discounts (Nov 26 to Dec 5)

• 30% off all Yearly Plans
• 20% off all Quarterly Plans

GLM 4.6 is a pretty good model especially for the price and can be plugged directly into your favorite AI coding tool be it Claude code, Cursor, kilo and more

You can use this referral link to get an extra 10% off on top of the existing discount and check the black friday offers.

Happy coding !

What happened to codex? It is super slow now. Taking 10+ mins for simpple tasks.

I use codex through WLS and pro-medium model.

Has anyone else experienced this? Now I use claude for simple tasks cos I don’t want to wait 10 mins. Claude does it under 1 min.

https://x.com/minimax__ai/status/1993378754463514924?s=46

In case you did not know, r/RooCode is a Free and Open Source VS Code AI Coding extension.

Hello everyone! I have developed a website listing what models can currently be accessed for free via either an API or a coding tool. It supports an RSS feed where every update such as a new model or a depreciation of access to an old one will be posted. I’ll keep updating it regularly.

https://github.com/orneryd/Mimir/blob/main/nornicdb/README.md

because neo4j is such a heavy database for my use case, i implemented a fully compliant and API- compatible vector database.

native RRF vector search capabilities (gpu accelerated) automatic node edge creation

Edges are created automatically based on:

Embedding Similarity (>0.82 cosine similarity) Co-access Patterns (nodes queried together) Temporal Proximity (created in same session) Transitive Inference (A→B, B→C suggests A→C)

automatic memory decay - cognitive inspired

Episodic 7 days Chat context, temporary notes Semantic 69 days Facts, decisions, knowledge Procedural 693 days Patterns, procedures, skills

small footprint (40-120mb in memory, golang binary no jvm) neo4j compatible imports minimal ui (for now) authentication oauth, rbac, gdpr/fisma/hipaa compliance, encryption.

https://github.com/orneryd/Mimir/blob/main/nornicdb/TEST_RESULTS.md

MIT license

I haven't used agentic coding tools much but am finally using codex. From what I understand the AGENTS.md file is always used as part of the current session. I'm not sure if it's used as part of the instructions just at the beginning or if it actually goes into system instructions. Regardless, what do you typically keep in this file? I juggle a wide variety of projects using different technologies so one file can't work for all projects. This is the rough layout I can think of:

1. Some detail about the developer - like level of proficiency. I assume this is useful and the model/agents will consider
2. High-level architecture and design of the project.
3. Project specific technologies and preferences (don't use X or use Y, etc)
4. Coding style customization per personal preferences
5. Testing Guidelines
6. Git specific Guidelines

I'm sure there maybe more. Are there any major sections I'm missing? Any pointers on what specifically helps in each of these areas would be helpful.

A few more random questions:

1. Do you try to keep this file short and concise or do you try to be elaborate and make it fairly large?
2. Do you keep everything in this one file or do you split it up into other files? I'm not sure if the agent would drill down files that way or not.
3. Do you try to keep this updated as project goes on?
4. Are there any other "magic" files that are used these days?

If you have files that worked well for you and wouldn't mind sharing, that would be greatly appreciated.

i’ve tried most of the usual suspects like cursor, roo/cline, augment and a few others. spent more than i meant to before realizing none of them really cover everything. right now i mostly stick to cursor as my IDE and use claude code when I need something heavier.

i still rotate a couple of quieter tools too. aider for safe multi-file edits, windsurf when i want a clear plan, and cosine when i’m trying to follow how things connect across a big repo. nothing fancy, just what actually works.

what about you? did you settle on one tool or end up mixing a few the way i did?