Trying to get into learning more about Hardware Security Modules and PKCS#11

working on a text game engine similar to Evennia: https://github.com/electroglyph/atheriz

moongate :) https://github.com/moongate-community/moongatev2 !

Against my better judgement, I'll post this landing page to a tool I'm working on:

https://wasm-drydock.dev

About an hour ago I was dismissed as AI slop on the r/rust Reddit. Whatever.

This tool is my line of defense in case `trunk` goes dead, which it seems to be increasingly likely. It helps me build fullstack sites using Actix Web and Yew.

Using it now to see if I can re-invent my blog site for the umpteenth time. :)

Building HEBBS — a memory engine for AI agents, written in Rust.

The problem: every agent framework bolts together a vector DB for recall, a KV store for state, maybe a graph DB for relationships, and then hopes the duct tape holds. You get one retrieval path (similarity search), no decay, no consolidation, and the agent forgets everything the moment context gets trimmed.

HEBBS replaces that stack with a single embedded binary (RocksDB underneath, ONNX for local embeddings). Nine operations in three groups: write (remember, revise, forget), read (recall, prime, subscribe), and consolidate (reflect, insights, policy). The interesting part is four recall strategies — similarity, temporal, causal, and analogical — instead of just "nearest vector."

Some technical decisions I'm happy with:

- No network calls on the hot path. Embeddings run locally via ONNX; LLM calls only happen in the background reflect pipeline.

- recall at 2ms p50 / 8ms p99 at 10M memories on a 2 vCPU instance.

- Append-only event model for memories — sync is conflict-free, and forget is itself a logged event (useful for GDPR).

- Lineage tracking: insights link back to source memories, revisions track predecessors.

SDKs for Python, TypeScript, and Rust. CLI with a REPL. gRPC + REST.

There's a reference demo — an AI sales agent that uses HEBBS for multi-session memory, objection handling recall, and background consolidation of conversation patterns.

Still early. The part I'm wrestling with now is tuning the reflect pipeline — figuring out when and how aggressively to consolidate episodic memories into semantic insights without losing useful detail. Curious if anyone working on agent memory has opinions on that tradeoff, or if you've found other approaches that work.

https://github.com/hebbs-ai/hebbs

I vibe coded a workout app that lives in the browser and is totally free unlike all the variants in the app store.

I did this in response to my trainer blowing me off.

Been spending quite a bit of time squashing bugs but it's now relatively stable and functional.

Check it out here, https://www.curlbro.com

We're building a new CRM from the ground up. We've helped a handful of companies and non-profits set up CRMs and it's amazing how bad existing CRMs are. It's like they don't understand what common day to day tasks need to be made as easy as possible.

We're also trying to use AI more thoughtfully than just bolting on a chatbot. We're planning to consider each workflow our customers need and how AI might help speed them up - even letting them build custom AI workflows. I think most businesses (especially smaller businesses) don't want to work at the level of Claude Code, Codex, etc. They want to work on higher level problems - build this dashboard, connect these data sources, invoice this customer, etc.

Aside from that, we've noticed that the basics really matter, so we're trying to nail that first.

We're definitely a bit delusional, we're just 3 people, we're doing it without funding and the competition is stiff, but we really believe in the product. Additionally, I think a lot of CRMs go south by taking on too much VC that naturally pushes them to prioritize ROI instead of continually improving the product.

Still working on Bitwit, my CS/math spaced-repetition education site [1]

I'm dogfooding it heavily. The bugs at this point tend to be in card formatting. I ended up delaying the introduction of TeX/MathJax until I had quite a few cards written, and man oh man, it's tedious to go back and fix that formatting in hundreds of cards.

The real question: does it work? I _think_ so. I'm learning, and I feel like I'm retaining more, and I think the general structure counteracts or compensates for most of my issues as a learner. I think the science is pretty solid, but I'm also experimenting in a few areas, so... eh, we'll see.

If anyone is interested in trying it out, you can use it for quite some time (several months, maybe a few years) without needing to provide an email address or sign up.

[1] https://bit-wit.com/

# My over-engineered console.log replacement is almost API/feature-stable: https://github.com/Leftium/gg

- Named `gg` for grep-ibility and ease of typing.

- However Claude has been inserting most calls for me (and can now read back the client-side results without any dev interaction!)

- Here is how Claude used gg to fix a layout bug in itself (gg ships with an optional dev console): https://github.com/Leftium/gg/blob/main/references/gg-consol...

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# I've been prototyping realtime streaming transcription UX: https://rift-transcription.vercel.app

- Really want to use dictation app in addition to typing on a daily basis, but the current UX of all apps I've tried are insufficient.

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# https://veneer.leftium.com is a thin layer over Google forms + sheets

- If you can use Google forms, you can publish a nice-looking web site with an optional form

- Example: https://www.vivimil.com

- Example: https://veneer.leftium.com/s.1RoVLit_cAJPZBeFYzSwHc7vADV_fYL...

- DEMO (feel free to try the sign up feature): https://veneer.leftium.com/g.chwbD7sLmAoLe65Z8