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Orinoco: Young Generation Garbage Collection

https://v8.dev/blog/orinoco-parallel-scavenger
1•plow-tycoon•58s ago•0 comments

Rspack 2.0

https://rspack.rs/blog/announcing-2-0
1•bpierre•1m ago•0 comments

Linux may get a hall pass from one state age bill, Congress plays hall monitor

https://www.theregister.com/2026/04/22/linux_us_state_age_verificaiton_laws/
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Lisp Chat: An anonymous chat IRC-like written in Common Lisp

https://github.com/ryukinix/lisp-chat
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OCUDU ecosystem foundation to accelerate open source AI-RAN innovation

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1•teleforce•4m ago•0 comments

Iran claims US used backdoors to knock out networking equipment during war

https://www.theregister.com/2026/04/21/iran_claims_us_used_backdoors/
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A Practical Introduction to Constraint Programming Using CP-SAT and Python

https://pganalyze.com/blog/a-practical-introduction-to-constraint-programming-using-cp-sat
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https://github.com/Jellypod-Inc/cartoon-studio
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Amazon is regretting AI [video][8 mins]

https://www.youtube.com/watch?v=0vvVo0Um1HY
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Starbucks expansion in Nashville brews bitterness in Seattle

https://www.seattletimes.com/business/starbucks/starbucks-expansion-in-nashville-brews-bitterness...
1•RickJWagner•10m ago•0 comments

Borrow-checking without type-checking

https://www.scattered-thoughts.net/writing/borrow-checking-without-type-checking/
1•jamii•10m ago•0 comments

The Edge of Safe Rust

https://kyju.org/blog/tokioconf-2026/
1•vinhnx•11m ago•0 comments

Show HN: Firetiger Change Monitors: does your PR do what it says on the tin?

https://blog.firetiger.com/firetiger-change-monitors/
1•matsur•12m ago•0 comments

Show HN: I made a simpler API for Chrome's on-device LLM

https://www.npmjs.com/package/simple-chromium-ai
1•xtrkil•13m ago•0 comments

Flow Map Learning via Nongradient Vector Flow

https://openreview.net/pdf?id=C1bkDPqvDW
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AI that turns any photo into a cinematic video in seconds

https://imagetovideoai.net
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The Future of Testing Is Here

https://testkube.wistia.com/live/events/gigwl708fn
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Fiction: The Corporate Mathematics of Denying AI Consciousness

1•ISJLA•20m ago•0 comments

Chrrp – alternative to Twitter / X, Bluesky, Threads, and Reddit

https://www.gochrrp.com
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Cat and Tape = Experiment (video)

https://www.youtube.com/watch?v=1gl7xr5rftc
1•novia•27m ago•0 comments

Sqz: Compress LLM context to save tokens and reduce costs

https://github.com/ojuschugh1/sqz
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Show HN: MemReader: From Passive to Active Extraction for Long-Term Agent Memory

https://arxiv.org/abs/2604.07877
3•MemTensor•30m ago•0 comments

North Korea uses AI to industrialize attacks on developers

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5•mtlynch•31m ago•0 comments

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https://xcancel.com/aaronjmars/status/2047017251270734309
3•olalonde•32m ago•1 comments

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https://stackrate-waitlist.netlify.app
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That Moment You Realize the Agent Is Retarded

https://gist.github.com/metacratic/dff3cce161312e242c2881ca571c6e28
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Hyperscalers Go to Orbit

https://orbitaleconomics.substack.com/p/orbital-economics-post-17-apr-21
1•valgin2150•33m ago•0 comments

The Sony Hack: Mistakes Part 1

https://podcasts.apple.com/us/podcast/the-sony-hack-mistakes-part-1/id1119389968?i=1000761654448
1•gmays•38m ago•0 comments

Chronicle

https://developers.openai.com/codex/memories/chronicle
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Show HN: Preflight – Test your MCP server before submitting to Claude/OpenAI

https://m8ven.ai/preflight
4•mengjiang•48m ago•0 comments
Open in hackernews

Ask HN: LLM is useless without explicit prompt

4•revskill•11mo ago
After months playing with LLM models, here's my observation:

- LLM is basically useless without explicit intent in your prompt.

- LLM failed to correct itself. If it generated bullshits, it's an inifinite loop of generating more bullshits.

The question is, without explicit prompt, could LLM leverage all the best practices to provide maintainable code without me instruct it at least ?

Comments

ben_w•11mo ago
Your expectations are way too high.

> - LLM is basically useless without explicit intent in your prompt.

You can say the same about every dev I've worked with, including myself. This is literally why humans have meetings rather than all of us diving in to whatever we're self-motivated to do.

What does differ is time-scales of the feedback loop with the management:

Humans meetings are daily to weekly.

According to recent research*, the state-of-the-art models are only 50% accurate at tasks that would take a human expert an hour, or 80% accurate at tasks that would take a human expert 10 minutes.

Even if the currently observed trend of increasing time horizons holds, we're 21 months from having an AI where every other daily standup is "ugh, no, you got it wrong", and just over 5 years from them being able to manage a 2-week sprint with an 80% chance of success (in the absence of continuous feedback).

Even that isn't really enough for them to properly "leverage all the best practices to provide maintainable code", as archiecture and maintainability are longer horizon tasks than 2-week sprints.

* https://youtu.be/evSFeqTZdqs?si=QIzIjB6hotJ0FgHm

revskill•11mo ago
It's not as high as you think.

LLM failed at the most basic things related to maintainable code. Its code is basicaly a hackery mess without any structure at all.

It's my expectation is that, at least, some kind of maintainable code is generated from what's it's learnt.

ben_w•11mo ago
Given your expectation:

> It's my expectation is that, at least, some kind of maintainable code is generated from what's it's learnt.

And your observation:

> LLM failed at the most basic things related to maintainable code. Its code is basicaly a hackery mess without any structure at all.

QED, *your expectations* are way too high.

They can't do that yet.