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hnup.date/hn-sota, a tool/pipeline to analyze Hacker News comments for popular coding models and related information.

Technical Positioning
Automates the process of identifying the latest and greatest coding models and harnesses from HN discussions, providing a quick overview for market intelligence.
SaaS Insight & Market Implications
This tool addresses a significant information overload problem for developers and product managers tracking the rapidly evolving AI coding landscape. By automating the extraction and analysis of Hacker News comments, it provides a focused, real-time overview of popular coding models and related infrastructure (harnesses, self-hosting, hardware). This offers a competitive intelligence advantage, allowing businesses to quickly identify emerging trends, assess market sentiment, and inform strategic decisions regarding AI tool adoption or development. The project's methodology, leveraging a data pipeline to distill collective developer opinion, demonstrates a practical application of data analysis for market insights, a valuable capability in the B2B SaaS space.
Proprietary Technical Taxonomy
coding assistants models harnesses automate this process quick overview popular coding models scan for harnesses self-hosting

Raw Developer Origin & Technical Request

Source Icon Hacker News May 3, 2026
Show HN: State of the Art of Coding Models, According to Hacker News Commenters

Hello HN,I was away from my computer for two weeks, and after coming back and reading the latest discussions on HN about coding assistants (models, harnesses), I felt very out of the loop. My normal process would have been to keep reading and figure out the latest and greatest from people's comments, but I wanted to try and automate this process.Basically the goal is to get a quick overview over which coding models are popular on HN. A next iteration could also scan for harnesses that people use, or info on self-hosting or hardware setups.I wrote a short intro on the page about the pipeline that collects and analyzes the data, but feel free to ask for more details or check the Google Sheet for more info.hnup.date/hn-sota

Developer Debate & Comments

cheesecakegood • May 3, 2026
It's extra interesting because I think the model people should be talking about is actually not DeepSeek V4 Pro, but the Flash version. When accounting for cache hits, the input price (per OpenRouter) is effectively only 6 cents per million tokens (3 vs 14 cents hit/miss), and 28 cents on output. That's really good efficiency, and it's not a sale price like they are doing with V4 Pro, it's the normal price.It's actually pretty difficult to find a good comparison model because there isn't one. Again, a 14/28 cent in/out model, ignoring cache, it scores just below GPT 5.4 Mini-xhigh (75/450) and Gemini 3 Flash (50/300) in intelligence. It's similar to Gemma 4 31B in some metrics (13/38) including cost, so it's not completely unheard of, but it's pretty notable that virtually everything else in the same region in most benchmarks are going to cost at least 5 times more (much, much more in very output-heavy contexts)
jesse_dot_id • May 3, 2026
I suspect companies are deploying bots to shift sentiment around their products. I find metrics like this to be largely useless vs. actually just trying stuff out.
Hari2028 • May 3, 2026
How noisy is the sentiment classification? Feels like that could skew results a lot
chillfox • May 3, 2026
Surely "Claude Opus 4.7" and "Claude Opus Latest" should be the same, right?
idivett • May 3, 2026
Thanks for doing the hard work. I've bookmarked this, hoping it'll come handy when new models are released. If you're taking feature requests, I've a few. - Show combined measurements of model makes. Like All claude models vs open ai, Deepseek so on. - Another toggle to remove the neutral section?
gobdovan • May 3, 2026
Before harnesses, I'd fix the methodology/claims. A saner methodology would be to see comments that compare two models, say 'gpt5.5>opus4.7' and infer context ('ctx:frontend', for example). For your current methodology, 'opus 4.6 was very smart, opus4.7 is a disappointing upgrade to 4.6' would make normal aspect-based sentiment analysis consider 4.6 is smarter than 4.6. But considering you have
2ndorderthought • May 3, 2026
Interesting to see the positive sentiment around kimi2.6 qwen3.6 and deepseek relative to the negative. I hope the trend of people appreciating open models continue. They aren't namesakes yet, but it's a higher percentage then I thought it would be. Especially on HN where we are all talking about businesses.I am upset because now anthropic, openai, meta, etc will continue their smear campaigns here. But I am also happy because it will make HN less useful when they do.Everything is a give and take I guess. Excited to see where the equilibrium sits
brooksc • May 2, 2026
It'd be interesting to also graph this over time to see how sentiment changes from when a model is released to today.
Jabbles • May 2, 2026
Please fix your graph so the names of the models are readable
jdw64 • May 2, 2026
Interpreting these metrics is quite interesting.One thing for sure is that while Claude is currently taking the spot in mentions, it carries a lot of negative sentiment due to API pricing policies and frequent server downtime. On the other hand, the runner-up, GPT-5.5, actually seems to have more positive feedback.Personally, my experience with Codex wasn't as good as with Claude Code (Codex freezes on Windows more often than you'd expect), so this is a bit surprising. That said, the more defensive GPT is definitely better in terms of sheer code-writing capability. However, GPT actually has quite a few issues with text corruption when generating in Korean or Chinese—something English-speaking users probably don't notice. In terms of model capabilities, when given the same agent.md (CLAUDE.md) file, I think GPT is better at writing code, while Claude is better at writing text during code reviews.Looking at the bottom right, Qwen and DeepSeek are open-source, so they are largely mentioned in the context of guarding against vendor lock-in, which drives positive sentiment. Considering that Hacker News occasionally shows negative sentiment toward China, the fact that they are viewed this positively—unlike US models—shows that being open-source is a massive advantage in itself.Anyway, one thing for sure is that Gemini is pretty much unusable.

Frequently Asked Questions

Market intelligence mapped to hnup.date/hn-sota, a tool/pipeline to analyze Hacker News comments for popular coding models and related information..

What is the technical positioning of hnup.date/hn-sota, a tool/pipeline to analyze Hacker News comments for popular coding models and related information.?
Based on our AI analysis of the original developer request, its primary technical positioning is: Automates the process of identifying the latest and greatest coding models and harnesses from HN discussions, providing a quick overview for market intelligence.
What is the general sentiment around hnup.date/hn-sota, a tool/pipeline to analyze Hacker News comments for popular coding models and related information.?
Yes, we have tracked 32 direct responses and active debates regarding this specific topic originating from Hacker News.
What are the foundational technologies related to hnup.date/hn-sota, a tool/pipeline to analyze Hacker News comments for popular coding models and related information.?
Our proprietary extraction maps hnup.date/hn-sota, a tool/pipeline to analyze Hacker News comments for popular coding models and related information. to adjacent architectural concepts including coding assistants, models, harnesses, automate this process.

Engagement Signals

66
Upvotes
32
Comments

Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like models and harnesses by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.