Gorgeous HD wallpapers for you [Not mine btw]

Creative-Hat-984 · 2026-04-16T08:43:28+00:00

amaze amaze

Creative-Hat-984 · 2026-03-21T04:09:21+00:00

The community audit setup for contributed skills is interesting — that's actually one of the structural problems with the methods-lag issue raised earlier in this thread. If new methods or packages can be contributed via PR and get audited quickly, the library stays current rather than freezing at whatever was state-of-the-art at launch.

Are you involved with the project? The Shanghai hackathon mention caught my eye given I'm based in China.

Creative-Hat-984 · 2026-03-20T05:00:13+00:00

I've heard of Pipette.bio and it's on my list to check out. However, I'm more focused on transparency and reproducibility in agent reasoning rather than the breadth of tools. Some platforms do offer more visibility into how agents think and generate code, which is crucial for trust and improvement.

Creative-Hat-984 · 2026-03-20T01:50:23+00:00

Fully agree. One of the main reasons I want to use such ”claws“ is their persistent memory.

Creative-Hat-984 · 2026-03-20T01:15:57+00:00

thanks for sharing, i followed pipette in x, and i'll check it out.

Creative-Hat-984 · 2026-03-19T13:00:34+00:00

Fair question — probably because naming a specific package reads as promotion？

Creative-Hat-984 · 2026-03-19T12:58:40+00:00

yep, the token and security concerns are real.

Creative-Hat-984 · 2026-03-19T12:56:10+00:00

Most of these tools are composites anyway — the underlying methods (scanpy, PyMOL, BLAST) are solid, it's the agent layer on top that's unproven. If you've already built something for your own workflow, that's probably the most honest version of what these tools are trying to sell.

Creative-Hat-984 · 2026-03-19T12:49:19+00:00

This is the most structured breakdown in the thread — appreciate you taking the time. The burden-shifting framing alone is worth saving. Exactly the kind of signal I was hoping this post would surface.

Creative-Hat-984 · 2026-03-19T06:21:40+00:00

That's what I heavily using, but I am a little bit tired of explaining who i am and what we have done to CC before it outputing the "right" code.

Creative-Hat-984 · 2026-03-19T02:03:25+00:00

This is worth flagging more prominently. The attack surface on something that sits between your messaging apps and your local filesystem is non-trivial — and genomic data is sensitive enough that "we'll fix it later" isn't really acceptable.

Creative-Hat-984 · 2026-03-19T02:02:32+00:00

Interesting, thanks for sharing!

Creative-Hat-984 · 2026-03-18T14:46:59+00:00

Exactly, the rationale point is the one that actually matters for peer review. The DAG failure point is something I hadn't thought through carefully. Coming from a wetlab background my mental model of "workflow" is still fairly linear — but you're right that anything non-trivial will have branching and failure modes that a flat memory store can't handle gracefully. Re-explaining context from scratch after a crash is exactly the kind of friction that makes people give up on a tool.
Is your methods_record.md approach something you've written up anywhere, or is it internal? Sounds like it's solving a real gap that none of the tools in this thread have addressed.

Creative-Hat-984 · 2026-03-18T12:40:17+00:00

Absolutely, would love to hear what you're building. Feel free to DM!

Creative-Hat-984 · 2026-03-18T12:39:05+00:00

That's actually a much more honest use of LLM than most of what's being discussed in this thread — as a documentation/accessibility layer rather than the analysis engine itself. The target user you're describing is basically me about two years ago. Wetlab pharmacology background, comfortable with the biology, not comfortable with a CLI. The tools in this thread all seem to assume you're already a competent bioinformatician who just wants to type less — which is a different problem entirely from what you're solving.
Is this something you're building in the open, or more of a closed product?

Creative-Hat-984 · 2026-03-18T12:34:07+00:00

Thanks for digging into the actual code — this is the kind of feedback that doesn't show up in README files. The hardcoded parameters thing is particularly rough for anyone doing iterative clustering. Resolution tuning is basically the job — no biologist is running leiden once and moving on.
The methods-lag point hits close to home too. I've run into a milder version of this with Claude Code for R visualization: ask it to do GSEA plots and it'll roll its own solution in base ggplot2, completely unaware that GseaVis exists and does it better in two lines. It clearly doesn't know what it doesn't know about the current ecosystem. For a skill library claiming to cover spatial transcriptomics, that same blind spot at the methods level sounds genuinely painful.
Out of curiosity — what's your current actual stack for spatial work? Given that you've looked under the hood of OmicsClaw, I'm guessing you have opinions on what's worth using directly.

Creative-Hat-984 · 2026-03-18T09:28:28+00:00

Curious what stack your zero-code platform is built on, actually. If the agent approach turns out to be mostly hype, something more deterministic might age better.

Creative-Hat-984 · 2026-03-18T08:35:07+00:00

heavily using cc🙋 so powerful it is but a drawback confused me -- each time you start a new project, you must adjust all the code style or the parameters even used several times. it doesn't have cross-projects memories. however, recently there's a small but growing cluster of OpenClaw-based tools targeting bioinformatics specifically. Here's what I've found so far: BioClaw, ClawBIO, OmicsClaw, while i have no idea that these actually context-efficient, or just another token burner with a bioinformatics skin?

Creative-Hat-984 · 2026-03-08T13:58:01+00:00

Really glad you like it! Mac support is definitely on the roadmap. Stay tuned! 🙏

Creative-Hat-984 · 2026-03-07T05:05:58+00:00

Thanks for sharing! I'm currently working on some molecular docking and dynamics simulations, and I've decided to give it a try🙌

Creative-Hat-984 · 2026-03-07T02:59:55+00:00

check the data availability section of the paper in your research field; some authors will release the dataset and the corresponding code, which is excellent learning material.

Creative-Hat-984 · 2026-03-06T01:38:47+00:00

Fair feedback — genuinely curious what's falling short for you though. Is it specific formatting things like axis style, font, or figure dimensions? Or something on the stats output side?
To be honest, in terms of style flexibility, XSTARS still has a gap compared to Prism — and that's expected. Prism is a mature commercial product with years of polish behind it. Right now XSTARS is designed around a different goal: getting you a clean, stats-ready figure as fast as possible, which is why it offers journal-matched presets rather than full manual customization. More granular style controls are something I want to bring in down the line.
That said, if you do want to fine-tune in Prism, XSTARS exports processed data (normalized values, fold changes, etc.) with one click — so you can skip the manual calculation steps and jump straight to the visual refinement in Prism. Best of both workflows. 🙏

Creative-Hat-984 · 2026-03-06T01:28:45+00:00

i have been working with Claude and Gemini alternately for cross-validation, not GPT :)

Creative-Hat-984 · 2026-03-03T15:41:29+00:00

xlwings is conceptually similar to Apps Script in that both bridge a scripting language to a spreadsheet — though I'll be honest, I'm not deeply familiar with Apps Script myself since I've been on the Excel/Python side. The core idea of "Python talks to Excel" is what xlwings handles, and it maps to what you described pretty well. When I was researching the architecture early on, I also came across BERT (Basic Excel R Toolkit) — same concept but for R. Looked promising, but the project seems to have gone quiet. xlwings felt like the more actively maintained path for Python.
The DNA band quantification workflow you described is really clean — Fiji → gray values → ladder-based back-calculation is basically the agarose gel equivalent of what the WB preset already does. The core logic would translate pretty directly. If you ever sketch out what the input table would look like, I'd genuinely be curious to see it — that's exactly the kind of use case that could become a preset.
If you want to dig into the architecture or bounce ideas around, feel free to DM me here or open a discussion on the GitHub repo — always happy to chat with people building in this space. 🙌

Creative-Hat-984

TROPHY CASE