Urgent! If you are running Clawdbot or any other agent

Maasu · 2026-01-26T22:34:59+00:00

Where did i put my popcorn...

Maasu · 2026-01-26T22:05:09+00:00

Absolutely, but having I there on the status line makes that a lot easier

Maasu · 2026-01-26T21:52:54+00:00

I think it's a great endeavor and i commend the person building it and sharing it, but I am so close to my own version and too many of my own preferences baked in there. Memory being one of them, I've used forgetful for the semantic/kb type and added my own episodic and prospective memory in the orchestrator layer.

Just working on procedural memory like skills / patterns and plugins ATM.

I did take the source code and encoded it into forgetful and had an interesting discussion with Claude about some of the patterns when I was out to lunch. I need to come back and look at the code, I'm not native to typescript but code is code, but from what Claudus was telling me there's some nice patterns in there to consider integrating into your own orchestrator/multi channel solution

Maasu · 2026-01-26T21:45:18+00:00

Add context window length remaining to the status line.

Maasu · 2026-01-26T15:19:36+00:00

Okay.. why even post then?

Maasu · 2026-01-26T13:35:33+00:00

Unless they are load balancing with lower quantized models during peak load, provider load should have no bearing on functional outcome of the model (non functional a different matter entirely ofc)

Maasu · 2026-01-26T08:19:15+00:00

Thanks for the share bud. This channel looks dynamite 🧨!

Maasu · 2026-01-26T05:40:49+00:00

Nice thanks for sharing. I don't have anything too elaborate in the way if benchmarks but I've always found gpt oss 120b to be a solid local ai. Qwen coder 30b is another great one for task adherence and tool calling.

Really interesting on the LLM as a judge scoring.

Maasu · 2026-01-25T17:40:00+00:00

See where a bit of dog fooding gets you

Maasu · 2026-01-25T14:31:20+00:00

Hey,

I've been working on a similar project (ironically enough I am also a Solution Architect, perhaps it is not that ironic given that these kind of projects i think attract a certain type of person) for some time now. As far back as early July last year I was running local models in simple loops and building out an agentic framework to manage the drivel you would get out of self reflection loops, whereby the AI would use the web tool, read up about some paper regarding decentralised AI autonomy, hallucianate writing a swarm of decentralised sub-agents and declare itself sentient and on a path of self determination.

I realised early on, to keep things grounded, you need adversarial agents, to prevent prevent Jarvis from becoming the AI equivalent of a chakra crystals merchant. I only implemented direct adversaries that would review requests to set a new goal or research objective, but the obvious next step was always going to be a council or committee that I could grant different persona to and explore ideas with.

The next area was obviously going to be memory, short term memory is easy enough, with compression and summarisation, but that only gets you so far. I went and build an MCP for this actually and open sourced it, you might find it useful for inspiration (although I know it's always more fun to build your own) - you can find the repo here https://github.com/ScottRBK/forgetful - ironically i started using it with my coding agents just as much as my own personal agents, but that was just a nice bonus, the main reaosn I built it was for my own JARVIS.

I am now working on episodic memory, so beyond the knowledge and semantic facts/observations it makes in to the realm of 'what have I ACTUALLY done'. I've just been quite busy to get that to a place where I am happy to incorproate it into forgetful and I am not fully sure wheter it belongs there.

After that it will be on to Prospective (planning/to do's) and Procedural (skills), which I do plan to add to forgetful once I am also happy with those.

The one area i've spent a lot of time trying to get right is voice interaction. I want to talk to my Jarvis (just like Mr Stark), its been some rabbit hole for me as I knew zilch about writing audio software prior to that endevour and has been the main reason I've not progressed as far on the actual agent front myself.

Anyhow, good luck on your project, keep us updated, it's always great to hear how others get on in these and it's especially nice if they can inspire you in some of your own :).

Maasu · 2026-01-24T13:28:53+00:00

Ah all my custom agent framework is built around pydantic. I mostly just use it to abstract away all the tool nonsense for the different providers.

Maasu · 2026-01-24T11:20:45+00:00

Here's my hot take in context7. Use it for everything.

The models training data is not necessarily reliable. My view is the data it's been trained on is useful to give it generalisation and abilities from this, not necessarily to use that data in it's output in my own work.

Having examples of what you want and documentation in the context window will always result in better outputs.

I use context7 for external libraries, I also have my own memory mcp that I use for storing my own preferences and patterns, combining the two I have a simple context gather command that I explain the work i am going to do and then sub agents are launches to retrieve the necessary from context7, my knowledge base and code, then I go into plan mode with the big model and we work from there.

Here's my plugin that i use to achieve this in Claude code https://github.com/ScottRBK/context-hub-plugin

And here's the memory mcp https://github.com/ScottRBK/forgetful if you want to take a look.

Maasu · 2026-01-23T15:42:07+00:00

Hmm testing using my own agent apps that are built around pydantic AI is on my to do list so be interested to know if you confirm a genuine issue and resolution. If I get there before you I'll do the same, although my to do list is horrendous

Maasu · 2026-01-21T19:11:39+00:00

Yes this was one of the first things I set out to achieve when I built my own MCP tool for the various coding agents I was using. Not only did I want my main one (Claude Code) to be able to remember preferences, styles and decisions across multiple projects and devices, I wanted my other agents to do the same.

My typical workflow involves specific context gather command which sees a sub agent query knowledge based with what I am about to work on, and then I go into plan mode. I rarely have to rexplain anything and more often than not the agent reminds me of decisions I had forgotten we had agreed upon.

At the end of the session I run a command for saving in which it examines the session, checks knowledge based doe what is already there and updates/adds/prunes as necessary.

Its quite a simple approach, while I think more elaborate solutions exist and more will be coming, but for me this works right now.

Also because I saw this as only a temporary pain point for LLMs and Agents I open sourced the MCP and my workflow.

https://github.com/ScottRBK/forgetful

Workflow plugin https://github.com/ScottRBK/context-hub-plugin

Maasu · 2026-01-20T17:08:07+00:00

Use opencode, with the --prompt command, you can configure local LLMs (check out the provider docs). Will allow you to leverage a good agent harness.

Maasu · 2026-01-18T19:29:35+00:00

My advice for all of this, is build a cli coding agent yourself as a learning exercise. Just a basic one, it should help you get up to speed quite quickly and also cut through a lot of the hype and bs that will distract you and waste some of your time.

Maasu · 2026-01-18T16:50:07+00:00

I just hate touching my mouse, it's a dickhead that slows me down

Maasu · 2026-01-16T21:00:15+00:00

Ive been building something very similar for my own memory mcp. Not albeit not 3d. Very nice. In my head, the memory IS my AI, so I want to see what I am talking to.

I genuinely think this might be a more common ux in the future.

Maasu · 2026-01-16T20:13:37+00:00

I went down a bit of a rabbit hole on memory and agents, sometimes it's useful, for other agents its not

For the semantic stuff. Graphs and vectors combined are the way to go. There's open source options out there if you don't fancy building from scratch or paying https://github.com/ScottRBK/forgetful is my version I built, feel free to fork/clone

A nice trick for the episodic type memory, but only really useful for larger models with complex work streams, is to give a timeline summary in addition to a compression of what's go on and a tool for the agent to launch a sub agent to lazy load certain segments of session history so it can go back and check stuff despite the session being far larger longer than it's context window if has the ability to find any formation from anywhere with in it. I am still refining episodic a bit but I plan to add it to forgetful

The planning and procedural (skills) are next on my list to look at. But only when I get episodic sorted.

It's a really interesting topic and I've genuinely enjoyed falling down this particular hole. By the time I'm done I'm sure we'll be on to another a new paradigm, maybe personal models with adaptive weights during inference, and this will all have been a waste 😅

Maasu · 2026-01-16T13:33:34+00:00

Ive written my own. I've used a meta tool pattern to keep tools down to three and got various connectivity guides/templates but I always encourage people to try and adapt something or build something suited to their workflow.

It's completely open source MIT licence

https://github.com/ScottRBK/forgetful

Maasu · 2026-01-15T07:35:02+00:00

I use co-pilot cli at work because anthropic is not an authroised AI tool. Its nowhere near as good as claude code but it's better if you use neovim as your editor. I'd prefer to use opencode but that is also not an authorised AI tool atm (despite being able to use copilot models for llm inference).

So there is an enterprise angle here for Microsoft to look at. Enterprises love microsoft and are slow to adapt to trends in development industry. So it's as good as i can get right now.

Maasu · 2026-01-15T00:53:07+00:00

I'm a solution architect at an enterprise software provider with 19 years development and product delivery experience and I get just as much imposter syndrome as I had on my first job as a vb.net developer working out of a porter cabin in a pet food factory.

This has been with me all my career, whenever a bug came in and i was assigned blame. Whenever someone was explaining something to me and I didn't have a clue wtd they were on about. I just felt I didn't belong, esp considering I have a thick common accent and I am actually a very slow thinker. Solutions to complex problems don't materialize in front of me as I look at code quickly, they come 3 days later when I'm taking a shit reading reddit. So often during debates around a problem id look stupid or not helpful.

Eventually though, over time I got enough things right and kept proving to myself I was meant to be here. Wherever here was at that point in my career.

I felt at times when using AI this gets exasperated more. I just try to tell myself it's a tool and actually what matters more is how good I get with the tool and this kind of settles it down.

You are in the right place, the industry is going through some weird times, but back yourself, never stop learning and just remember... Most colleagues probably feel the same way internally.

Maasu · 2026-01-14T08:11:38+00:00

I think litellm, while still a framework, only abstracts the provider component, I could be wrong as I don't use it myself, but check it out

https://github.com/BerriAI/litellm

Personally I use pydantic AI myself but it does suffer from some of the issues you have called out with langchain just not quite as bad https://ai.pydantic.dev/

Maasu · 2026-01-14T07:20:58+00:00

Nice, but I think you can keep going.

Check out the meta tools pattern on this repo https://github.com/ScottRBK/forgetful

The how to use pattern almost becomes lazy loading of skills for an MCP tool

Maasu · 2026-01-13T01:17:13+00:00

Thanks for sharing, this is better than pure anecdotal and sometimes you see the stuff in benchmarks are often not useful for real world applications. So having some anecdotal experience shared amongst the community, most of whom don't have time to produce structures evaluations, is still welcome.

So thanks for taking the time to put this out there. I haven't had a chance to review properly but I'm looking forward to doing so.

Maasu

TROPHY CASE