Suvash Thapaliya

Suvash Thapaliya

@suva.sh

programming & etcéteras

633 followers122 following82 posts

A bit embarrassing to admit, but another thing I've been rather late at using/understanding is WebAuthn(FIDO2) compared to U2F(FIDO). Not having used a FIDO2 compatible hardware key, I had sort of mentally bucketed them together. But, FIDO2 is a pretty solid improvement/extension over the FIDO. TIL.

I've been using the same #GPG keys (master, sign., enc. & auth.) on a @yubico.com Yubikey 4 since 2017, (following the drduh guide) extending the expiry every X years. I'm now considering creating new keys (esp. for RSA/4096 & ed25519) for a new #Yubikey 5. How is everybody else going about this?

I've also been considering using Age for encryption to try out modern tooling, (though I'm comfortable with GPG+Yubikey) and only recently learnt that there might be a pathway to use Age keys on the PIV slots. @filippo.abyssdomain.expert Curious if this is the best way to use Age+Yubikeys?

Super late to the "Gigabit at home" party, but recently updated to it, also moved from Synology to Ubiquiti Router+AP setup, Ethernet where possible. Finally I can max out on downloading these bulky models from HF & Ollama store. 😅

Also, @ubiquiti.bsky.social hardware+software is really well done. A lot of things that I used to have sidecars & other solutions for is now just covered with UCG & U6+, and the Unifi software. What a treat really, and I'm pretty sure I haven't even started using all of it.

Just finished reading “House of Huawei”. What a solid read, would absolutely recommend to anyone trying to understand and forward connect the dots to where we are with technology wars right now.

Right after the Huawei book, I finally picked a copy of “Chip War”. I think this book honestly is the most readable compressed history of the chip industry all the way from vacuum tubes to modern day custom accelerator chips incl. the geopolitics. Not to drop the momentum, what should I read next?

Solid 10/10 post by Thorsten, on letting the results of your LLM(http) calls decide what function to execute next, in a loop, in a loop, ....

Thorsten Ball@thorstenball.com

Finally did it. I wrote down how to build a code-editing agent. In 315 lines of code. And yes, it works. Very well. There is no moat. Read it here: ampcode.com/how-to-build...

Been meaning to use the latest Gemini(2.x series) models for a while now. Perfect timing by @strickvl.bsky.social with this set of practical examples in this standalone site. The only thing that could have made this even better for me would be direct curl examples, but maybe I'm asking too much. 😅

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Last week I read a bunch of articles, all of which were a fun read. Not so heavy on social/sharing etc. these days, but figured I'd share them here regardless, because these were a lot of fun to read slowly through.

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Last week I read a bunch of articles, all of which were a fun read. Not so heavy on social/sharing etc. these days, but figured I'd share them here regardless, because these were a lot of fun to read slowly through.

Gemma3 family of models seem quite good for straight-up text extraction from images(those horrible pdfs). Along those lines, I remember olmOCR from @ai2.bsky.social released just some weeks ago, based on Qwen VL models. Curious if somebody is working on olmOCR pipeline with Gemma3 models.

This is one of the first few posts I've seen that uses Deepseek model to generate high quality datasets, which then can be used to train the ModernBERT models. Really neat stuff! Once can easily replace the slower, expensive 3rd party LLM router with a fast, cheap & local model.

Daniel van Strien@danielvanstrien.bsky.social

Why choose between strong #LLM reasoning and efficient models? Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT for fast, efficient classification. New blog post: danielvanstrien.xyz/posts/2025/d...

One of the most fun things about using Open WebUI is how you can intersperse various models in the same conversation, depending on the next task you intend to work on. Start off with `deepseek-r1`, and the follow up the conversation with something like `phi4` or `qwen2.5-coder`.

How are folks building the "memory" layer in their #llm augmented #workflows, esp. in the context of multi step workflows? Most of the simple examples I've seen share the memory across all steps, but this doesn't seem "clean". Reminds me of workflow steps all reading/writing from same blob storage.

If done correctly, each of the steps in the workflow has its distinct responsibility, thus it's own prompt+context+query+tools+etc..., the history of which obviously doesn't make sense to be shared across multiple steps. Each step having it's own explicit "memory" makes more sense to me. Thoughts?

From my own experience building LLM augmented systems this year, I've been telling a lot of my colleagues that most use cases don't really need an "agent". Instead, they need a well defined product workflow with models(llms etc.) in the mix. It's reassuring to read the recent @anthropic.com blog...

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I probably read this blog multiple times today. You can really tell that it's written by a team that cares about helping engineers build simple & reliable systems. Thanks again to the authors Erik Schluntz and Barry Zhang. I wish more of the @anthropic.com team would be here on Bsky.

From my own experience building LLM augmented systems this year, I've been telling a lot of my colleagues that most use cases don't really need an "agent". Instead, they need a well defined product workflow with models(llms etc.) in the mix. It's reassuring to read the recent @anthropic.com blog...

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And then, there's a pretty good coverage on building agents. Once again, after a good amount of warning that most systems don't need to be designed this way. Sure, agentic systems sound more fun. But, from what I see, most real world problems don't need autonomous plan-attempt-verify loops.

Finally, the blog summarizes by once again warning against complexity. LLM augmented systems are already hard enough to evaluate, it's only sensible to keep things simple outside the LLM boundaries.

From my own experience building LLM augmented systems this year, I've been telling a lot of my colleagues that most use cases don't really need an "agent". Instead, they need a well defined product workflow with models(llms etc.) in the mix. It's reassuring to read the recent @anthropic.com blog...

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I appreciate that they've introduced names for some common workflows. I've already been using Prompt Chaining & Routing, considering Parallelization next. More importantly, notice that they're not new hypey names, these flows already existed in the data engineering world.

And then, there's slightly complex Orchestrator-worker & Evaluator-optimizer workflows. I've been thinking of using something like Evaluator-optimizer for steps that could fail or produce unwanted results, but haven't gotten there yet. Either ways, it's useful to have names for these patterns.

From my own experience building LLM augmented systems this year, I've been telling a lot of my colleagues that most use cases don't really need an "agent". Instead, they need a well defined product workflow with models(llms etc.) in the mix. It's reassuring to read the recent @anthropic.com blog...

...where they walk readers cleanly through a lot of this noise (right now & probably all of 2025). I really love the fact that they keep iterating for simplicity. When starting out, there's no good reason to inherit complexity(langchain etc.), esp. if you plan to have end-to-end control over it.

Huh! Maybe finally time to switch to my own domain, so I don't have to create another account just to "squat" my original username here.

Bluesky@bsky.app

📢 App Version 1.96 is rolling out now (1/6) In this release: a notifications Mentions tab, reserving your default username when you verify your account with a domain, and other improvements!

@bsky.app btw, not sure if feature or a bug, but I noticed that if you verify multiple handles(say one after another) with the TXT dns entry(and leave the entry there), then both those handles resolves to the profile. Maybe there's an expiry time for the old domain handle, or maybe not?

Suvash Thapaliya reposted

I'll get straight to the point. We trained 2 new models. Like BERT, but modern. ModernBERT. Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff. It's much faster, more accurate, longer context, and more useful. 🧵

Given the new announcement of the Willow #quantum chip, I went ahead and rewatched this introductory lecture after a couple of years. Love the energy and the "no-nonsense, let's build up from the basics" approach by the presenter. Any follow-up recommendations? www.microsoft.com/en-us/resear...

microsoft.comQuantum Computing for Computer Scientists - Microsoft ResearchThis talk discards hand-wavy pop-science metaphors and answers a simple question: from a computer science perspective, how can a quantum computer outperform a classical computer? Attendees will learn ...

Looks like a lot more activity here these days. Not sure where/how everybody's finding people here, but 👋 to everybody who sees this. And yes, I've heard about starter packs and automated feeds, haven't quite used them yet, what else have I missed?

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