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Tencent-Hunyuan
AutoCodeBenchmark
Python2025–2026
Tencent-Hunyuan / AutoCodeBenchmark

AutoCodeBenchmark

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10Distinct people
13Mentions
0Explicit recommenders
438GitHub stars
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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month13 dated mentions

Observed mentions: 2025-09-02 06:04:23 UTC to 2026-07-14 14:46:56 UTC.

01232025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsSep 20252025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 commentsFeb 20262026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC220
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC101
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC110
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC303
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC303
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC101
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC101

What Hacker News says

13 sources
Leftium·Jul 14, 2026
Theo video based on data from Tencent's Autocodebench: https://youtu.be/iV1EcfZSdCM Conclusion: Elixir was the best (had the highest problem solve rate). Reasons (Theo's interpretation): - code collocation, where documentation is integrated directly within the source code - design philosophy of a language (readability, clear idioms, and strict expectation management) --- Sources from video: - https://github.com/Tencent-Hunyuan/AutoCodeBenchmark/tree/ma... - https://martinalderson.com/posts/which-programming-languages... - https://simonwillison.net/2026/Jan/19/nanolang/#atom-everyth...
In Ask HN: Any objective research on which languages are best for AI agents?
josevalim·Jun 4, 2026
> Honest question, in the era of vibe and AI assisted coding is there any advantages of using untyped programming languages, apart from the fact that non-typed languages has more traning data for the LLM? Author here. Type systems restrict which programs can be expressed and increasing expressiveness often requires increasing type-system complexity (which, speaking from experience, both humans and agents will struggle with). Plus they are not the only mechanism to assert correctness (they only validate a subset of your program correctness and do not replace tests) and you are still on your own when it comes to actually recovering from unexpected errors (something Erlang/Elixir were designed for). I'd say there are two flip sides to your question: 1. Given types do not replace tests, if you can use AI to automate full test coverage, are th
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ere actual benefits in static typing for coding agents? The downside of tests for humans is that we suck at writing them (but guided agents can do better) and they can take time to run (which agents do not care) 2. Do we actually have any data or evaluations that show which typing discipline is better for agents? The only benchmark I am aware of [AutoCodeBenchmark] has Elixir come first (dynamic) and C# as second (static), so it doesn't answer the question. There are other benchmarks that show dynamic languages require fewer tokens to solve problems (but that's not a metric I particularly care about) My gut feeling is that local structure, documentation, quality and quantity in the training data, etc are likely to play a more important role than typing for coding agents. I'd also love to measure how agents perform on specific domains. If you are writing concurrent software, how does Elixir/Java/Rust/Go compare? But without data, it's hard to say. [AutoCodeBenchmark]: https://github.com/Tencent-Hunyuan/AutoCodeBenchmark
In Elixir v1.20: Now a gradually typed language
HorizonXP·Feb 19, 2026
So I know these are just benchmarks, but apparently Elixir is one of the best languages to use with AI, despite having a smaller training dataset: https://www.youtube.com/watch?v=iV1EcfZSdCM and https://github.com/Tencent-Hunyuan/AutoCodeBenchmark/tree/ma... Furthermore, it's actually kind of annoying that the LLMs are not better than us, and still benefit from having code properly typed, well-architected, and split into modules/files. I was lamenting this fact the other day; the only reason we moved away from Assembly and BASIC, using GOTOs in a single huge file was because us humans needed the organization to help us maintain context. Turns out, because of how they're trained, so do the LLMs. So TypeScript types and tests actually do help a lot, simply because they're deterministic guardrails that the LLM can use to check its work and b
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e steered to producing code that actually works.
In Bridging Elixir and Python with Oban
bnchrch·Feb 2, 2026
Swift has all the things I want in a language - Strong Typing - Great Performance - Actor Model Concurrency [0] - Modern Ergonomics - Corporate Backing - Performance - Functional Style - LLMs perform well with it [1] - Usable across iOS, Android, Web, and Browsers [2][3] The only thing its missing is adoption outside of the iOS space. I'm not sure it will be able to make that leap, but the ingredients are there. If it does I'd be happy to make it my primary language. ---------------- [0] https://www.hackingwithswift.com/quick-start/concurrency/wha... [1] https://github.com/Tencent-Hunyuan/AutoCodeBenchmark/blob/ma... [2] https://www.swift.org/blog/nightly-swift-sdk-for-android/ [3] https://vapor.codes/
In Swift in the Browser with ElementaryUI (Swift FOSDEM 2026 Talk) [video]
aeonfox·Oct 15, 2025
Enjoyed this balanced perspective on Elixir. In regards to this: > My bet? The language with the best feedback loop to LLMs wins. TypeScript has a huge advantage here. Static types give LLMs clear signals about correctness. Tencent designed their own methodology of testing LLMs across languages. Elixir topped the lot on AutoCodeBench in terms of problems solved correctly: https://github.com/Tencent-Hunyuan/AutoCodeBenchmark?tab=rea... An alternate bet might be that context efficiency wins. The more expressive, comprehensible, and idiomatic languages have the advantage.
In Building with Elixir for Three Years: A Production Retrospective

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