abrichr

Joined 15 years ago

Karma
1,463
Submissions
650
Member since
Nov 16
Machine learning scientist, engineer, and entrepreneur.

- https://github.com/OpenAdaptAI/OpenAdapt - https://openadapt.ai - https://linkedin.com/in/richard-abrich - https://github.com/abrichr - https://MLDSAI.com - richard at openadapt dot ai

abrichr.at.hn

meet.hn/city/ca-Old-Toronto

Socials: - github.com/abrichr - x.com/abrichr - linkedin.com/in/richard-abrich

---

View on Hacker News

Recent submissions

Comment · 2 months ago
I’ve been working on OpenAdapt: https://github.com/OpenAdaptAI

It’s an open source attempt at making GUI automation reliable enough for things like EHRs, ERPs, Citrix apps, etc.

A lot of agent approaches basically keep looking at the screen and asking a model what to do next. We’ve gone in a different direction. We use models to learn or repair a workflow, but once it works we try to compile it down into something deterministic. On a normal run there can be zero model calls.

Lately I’ve been working on figuring out how to know that an action actually had the intended effect, rather than just that the click/keystroke happened. So workflows have preconditions, postconditions/evidence checks, and halt if the state is ambiguous. We also recently added a way to send those halted decisions to a phone for a human to approve/reject.

Comment · 8 months ago
ChatGPT Deep Research dug through Taalas' WIPO patent filings and public reporting to piece together a hypothesis. Next Platform notes at least 14 patents filed [1]. The two most relevant:

"Large Parameter Set Computation Accelerator Using Memory with Parameter Encoding" [2]

"Mask Programmable ROM Using Shared Connections" [3]

The "single transistor multiply" could be multiplication by routing, not arithmetic. Patent [2] describes an accelerator where, if weights are 4-bit (16 possible values), you pre-compute all 16 products (input x each possible value) with a shared multiplier bank, then use a hardwired mesh to route the correct result to each weight's location. The abstract says it directly: multiplier circuits produce a set of outputs, readable cells store addresses associated with parameter values, and a selection circuit picks the right output. The per-weight "readable cell" would then just be an access transistor that passes through the right pre-computed product. If that reading is correct, it's consistent with the CEO telling EE Times compute is "fully digital" [4], and explains why 4-bit matters so much: 16 multipliers to broadcast is tractable, 256 (8-bit) is not.

The same patent reportedly describes the connectivity mesh as configurable via top metal masks, referred to as "saving the model in the mask ROM of the system." If so, the base die is identical across models, with only top metal layers changing to encode weights-as-connectivity and dataflow schedule.

Patent [3] covers high-density multibit mask ROM using shared drain and gate connections with mask-programmable vias, possibly how they hit the density for 8B parameters on one 815mm2 die.

If roughly right, some testable predictions: performance very sensitive to quantization bitwidth; near-zero external memory bandwidth dependence; fine-tuning limited to what fits in the SRAM sidecar.

Caveat: the specific implementation details beyond the abstracts are based on Deep Research's analysis of the full patent texts, not my own reading, so could be off. But the abstracts and public descriptions line up well.

[1] https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...

[2] https://patents.google.com/patent/WO2025147771A1/en

[3] https://patents.google.com/patent/WO2025217724A1/en

[4] https://www.eetimes.com/taalas-specializes-to-extremes-for-e...

Comment · 10 months ago
Source? How recently?
Comment · 10 months ago
You can reach much higher spend through the API (which you can configure `$claude` to use)
Comment · 11 months ago
Comment · 11 months ago
Comment · 1 year ago
Comment · 1 year ago
> But that one year of knowledge building, distribution, early customers, then dominates who has control over the cap table for a decade.

Can you recommend any resources for learning how to do this work yourself?

Comment · 1 year ago
> While the LLMs get to blast through all the fun, easy work at lightning speed, we are then left with all the thankless tasks: testing to ensure existing functionality isn’t broken, clearing out duplicated code, writing documentation, handling deployment and infrastructure, etc.

I’ve found LLMs just as useful for the "thankless" layers (e.g. tests, docs, deployment).

The real failure mode is letting AI flood the repo with half-baked abstractions without a playbook. It's helpful to have the model review the existing code and plan out the approach before writing any new code.

The leverage may be in using LLMs more systematically across the lifecycle, including the grunt work the author says remains human-only.

Comment · 1 year ago
Comment · 1 year ago
Corporate income taxes are treated differently than personal income taxes. You absolutely can deduct corporate expenses in Canada.

Live from the official Hacker News API

A modern reader for Hacker News · SvelteKit + Skeleton