Best topics to create a new blog: February 2026

Show HN: I wrote a technical history book on Lisp
28 by cdegroot | 2 comments on Hacker News.
The book page links to a blog post that explains how I got about it (and has a link to sample content), but the TL&DR is that I could not find a lot of books that were on "our" history _and_ were larded with technical details. So I set about writing one, and some five years later I'm happy to share the result. I think it's one of the few "computer history" books that has tons of code, but correct me if I'm wrong (I wrote this both to tell a story and to learn :-)). My favorite languages are Smalltalk and Lisp, but as an Emacs user, I've been using the latter for much longer and for my current projects, Common Lisp is a better fit, so I call myself "a Lisp-er" these days. If people like what I did, I do have plans to write some more (but probably only after I retire, writing next to a full-time job is heard). Maybe on Smalltalk, maybe on computer networks - two topics close to my heart. And a shout-out to Dick Gabriel, he contributed some great personal memories about the man who started it all, John McCarthy.

Sunday, February 15, 2026

Technology

Ex-Tech –> Homeless in SF
43 by Zamoshi | 2 comments on Hacker News.

Saturday, February 14, 2026

Technology

Ars Technica makes up quotes from Matplotlib maintainer; pulls story
52 by robin_reala | 10 comments on Hacker News.

Friday, February 13, 2026

Technology

Cogram (YC W22) – Hiring former technical founders
1 by ricwo | 0 comments on Hacker News.

Wednesday, February 11, 2026

Technology

Mamdani Hires Lisa Gelobter as Chief Tech Officer
38 by leephillips | 20 comments on Hacker News.

Tuesday, February 10, 2026

Technology

CoLoop (YC S21) Is Hiring Ex Technical Founders in London
1 by mrlowlevel | 0 comments on Hacker News.

Technology

MIT Technology Review has confirmed that posts on Moltbook were fake
44 by helloplanets | 9 comments on Hacker News.

Sunday, February 8, 2026

Technology

In the AI gold rush, tech firms are embracing 72-hour weeks
33 by yladiz | 32 comments on Hacker News.

Technology

Modern and Antique Technologies Reveal a Dynamic Cosmos
4 by sohkamyung | 0 comments on Hacker News.

Friday, February 6, 2026

Technology

Show HN: BioTradingArena – Benchmark for LLMs to predict biotech stock movements
9 by dchu17 | 1 comments on Hacker News.
Hi HN, My friend and I have been experimenting with using LLMs to reason about biotech stocks. Unlike many other sectors, Biotech trading is largely event-driven: FDA decisions, clinical trial readouts, safety updates, or changes in trial design can cause a stock to 3x in a single day ( https://ift.tt/jIlnFA3... ). Interpreting these ‘catalysts,’ which comes in the form of a press release, usually requires analysts with previous expertise in biology or medicine. A catalyst that sounds “positive” can still lead to a selloff if, for example: the effect size is weaker than expected - results apply only to a narrow subgroup - endpoints don’t meaningfully de-risk later phases, - the readout doesn’t materially change approval odds. To explore this, we built BioTradingArena, a benchmark for evaluating how well LLMs can interpret biotech catalysts and predict stock reactions. Given only the catalyst and the information available before the date of the press release (trial design, prior data, PubMed articles, and market expectations), the benchmark tests to see how accurate the model is at predicting the stock movement for when the catalyst is released. The benchmark currently includes 317 historical catalysts. We also created subsets for specific indications (with the largest in Oncology) as different indications often have different patterns. We plan to add more catalysts to the public dataset over the next few weeks. The dataset spans companies of different sizes and creates an adjusted score, since large-cap biotech tends to exhibit much lower volatility than small and mid-cap names. Each row of data includes: - Real historical biotech catalysts (Phase 1–3 readouts, FDA actions, etc.) and pricing data from the day before, and the day of the catalyst - Linked Clinical Trial data, and PubMed pdfs Note, there are may exist some fairly obvious problems with our approach. First, many clinical trial press releases are likely already included in the LLMs’ pretraining data. While we try to reduce this by ‘de-identifying each press release’, and providing only the data available to the LLM up to the date of the catalyst, there are obviously some uncertainties about whether this is sufficient. We’ve been using this benchmark to test prompting strategies and model families. Results so far are mixed but interesting as the most reliable approach we found was to use LLMs to quantify qualitative features and then a linear regression of these features, rather than direct price prediction. Just wanted to share this with HN. I built a playground link for those of you who would like to play around with it in a sandbox. Would love to hear some ideas and hope people can play around with this!