MurphyA/hacker-news huggingface dataset:
by alessioalex | 2026-03-09 12:11:50+00:00
A macOS screen recording CLI application that can record: screen, mic(s), camera(s), system audio, iPhone/iPad screen. You can select different tracks with their own options (video encoding, audio encoding, filename, combine camera with mic for example or even screen + system audio + mic). It also tracks clicks, scroll, keyboard and generates closed captions automatically at the end. Now I'm working on adding 1-2 more features and an MVP is done.
by hrmtst93837 | 2026-03-09 12:12:00+00:00
Swapping ProcessPoolExecutor for ThreadPoolExecutor gives real memory and IPC wins, but it trades process isolation for new failure modes because many C extensions and native libraries still assume the GIL and are not thread safe.

Measure aggressively and test under real concurrency: use tracemalloc to find memory hotspots, py-spy or perf to profile contention, and fuzz C extension paths with stress tests so bugs surface in the lab not in production. Watch per thread stack overhead and GC behavior, design shared state as immutable or sharded, keep critical sections tiny, and if process level isolation is still required stick with ProcessPoolExecutor or expose large datasets via read only mmap.
by ahartmetz | 2026-03-09 12:12:21+00:00
This is one of these cases like gun crime where:

USA: There is no solution!

Rest of world: slightly embarrassed look

There are legal terms and concepts like good faith, expected and unexpected terms, reasonable expectations, abuse of a legally unsophisticated party and so on. In other countries, neither the fiction that everyone reads or is expected to read the 10-page "dining contract" of a restaurant exists nor is it allowed (enforceable) to put any unrelated or unreasonable crap in there.
by redfloatplane | 2026-03-09 12:12:34+00:00
I think that's going to be very, very hard to sell to many people here in rural Ireland (Roscommon in my case). I would really love to see people stop burning turf but it's such a strong cultural thing that in some parts you'd be ostracised for even thinking the thought.

I've personally spoken to people (who are otherwise quite environmentally aware) who suggest they'd never vote for the Green Party because they'd take their turf away. It's a tough sell.
Show HN: I built a clipboard mgr with crypto address swap attack detection
by revastas | 2026-03-09 12:12:35+00:00
Yankput is a Mac clipboard manager built for developers. The core idea: your clipboard is a security hole and nobody is watching it.
The feature that started it all — crypto address swap protection. Malware silently replaces crypto addresses in your clipboard while you're pasting. Yankput detects when a copied address changes before you paste and fires a system alert.
It also detects and masks secrets (API keys, GitHub tokens, AWS keys, etc.) and auto-deletes them after 5 minutes. Plus an Incognito mode that pauses recording entirely.
On top of that: JSON/XML formatter, Base64 encoder, SHA hash generator, regex tester, and line diff — all operating on whatever's in your clipboard.
$14.99 one-time. yankput.app
Would love feedback from the HN crowd, especially on the security angle.
by staticman2 | 2026-03-09 12:12:38+00:00
There is no right to vote in the constitution as written and interpreted in the 1700s. There is also no guarantee of freedom of speech. The first amendment was considered a rule that only applied federally.

What's democratically illegitimate is everything you wrote in this thread.

If your state government threw you in jail for what you just wrote that would be perfectly aligned with your "original understanding" interpretation of the U.S constitution.
by palata | 2026-03-09 12:12:57+00:00
> even during the window of opportunity with Scala on Android.

I don't understand this. You can run any pure Java jar on Android, pretty sure you can do that with Scala too? It's not exactly a "different platform" in terms of programming language. Sure it needs tooling and specific libraries, but that's higher level than the programming language.

Jetbrains is doing interop with Swift (Kotlin -> ObjC -> Swift and more recently Kotlin -> C -> Swift), which Scala never did. But I don't really see how this is relevant in this conversation.
by whatl3y | 2026-03-09 12:13:15+00:00
I'm most excited about reducing friction for digital payments of APIs and resources in the agentic commerce world.

I think the first step is standardizing HTTP 402 using traditional, familiar payment rails like Stripe, then we can move to things like on-chain or other rails later.

I am building https://stripe402.com to try to make it dead simple for those building APIs/resources to get paid per request through stripe without user's needing to sign up for accounts, get API keys, any of that normal painful workflow required today.

Check it out and feedback welcome!
by pu_pe | 2026-03-09 12:13:41+00:00
The transcript from ChatGPT they shared is interesting [1]. Only a few messages from the user were needed, mostly to present the problem correctly. The most meaningful one was a review on a preliminary report, made by another ChatGPT instance. The problem was solved and a full technical report was written in a total time of 2-3h.

[1] https://chatgpt.com/share/69aaab4b-888c-8003-9a02-d1df80f9c7...
Show HN: Kairos, real-time AI who cross-verifies (Python, 100KB)
by joshuaveliyath | 2026-03-09 12:14:01+00:00
Hi HN,
I'm Joshua, a teen from Kerala, India.

I built Kairos because I was frustrated that every AI I tested hallucinated on live events. During today's T20 World Cup Final, ChatGPT named the wrong player. Copilot named a different wrong player. Both confidently. Neither had live data.

Kairos solves this differently. Instead of sending raw search results to the LLM and hoping, it runs a verification pass first — cross-checking titles across RSS, DuckDuckGo, and NewsAPI, scoring each result by how many independent sources confirm it, then sorting by confidence before the LLM ever sees the context.

The architecture:

1. Pronoun resolution from ChromaDB conversation history (no API call) 2. Domain classification (6 domains) 3. Query expansion — 1 query becomes 4 targeted searches, pure Python, zero extra API calls 4. Parallel async fetch with timeouts 5. Cross-verification scoring 6. Dynamic thinking budget (0 for sports scores, up to 10k for complex analysis, hard capped) 7. 250 word output limit

Total codebase: ~90KB Model: Gemini 2.5 Flash Cache: ChromaDB Cost: $0

Benchmark on today's T20 Final: Kairos 43/50, Gemini 40/50, Perplexity 38/50, Copilot 26/50, ChatGPT 19/50

ChatGPT and Copilot both hallucinated player names with full confidence. Kairos cited 15 live sources.

GitHub: https://github.com/joshuaveliyath/kairos

Happy to answer technical questions.
by geon | 2026-03-09 12:14:53+00:00
As I understand it, this only works with still images that scroll vertically.

Each revolution is one frame, so if you compare the data paths next to each other, they don't make up an image but the same single line of several consecutive frames.

Scrolling a still image makes the same line on the screen cover a different part of the image each frame, so you can sort of make out what the original image looked like.

The end credits should show up as a single tall image, since the only limit to the height is the radius of the disc.
by jacquesm | 2026-03-09 12:15:24+00:00
Interesting, that case was just withdrawn a few days ago:

https://www.allergicliving.com/2026/03/03/lawsuit-against-di...

"Disney dropped its bid to force arbitration over the streaming service’s clause in August 2024, following a barrage of public backlash."

And not because it was a clearly outrageous thing to do.
by throwaway132448 | 2026-03-09 12:16:15+00:00
I found the article confusing. Its premise seems to be that alternative methods to deep learning “work”, and only faded out due to other factors, yet keeps referencing scenarios in which they demonstrably failed to “work”. Such as:

> In 2012, Alex Krizhevsky submitted a deep convolutional neural network to the ImageNet Large Scale Visual Recognition Challenge. It won by 9.8 percentage points over the nearest competitor.

Maybe there’s another definition of “works” that’s implicit and I’m not getting, but I’m struggling to picture a definition relevant to the history-of-deep-learning narrative they are trying to explain.
by ben_w | 2026-03-09 12:16:19+00:00
Cuts/scene transitions: 5s, 7s, 8s, 11s, 13s, 14s, 17s, 20s, 25s, 27s, 29s, (and then several continuously overlapping transitions), 36s, 38s, 39s, 40s, 42s, 43s, 47s, 49s, 50s, 52s, another at 52s, 54s, two more at 55s, two at 57s, 1:00, …

This is demonstrating the specific limitation I was talking about. And also, some of those scenes, if you pause on them, they've got the slightly-off vibes of GenAI content, a casual viewer is only missing that because the scenes change so quickly. Something something moonwalking gorilla.

An artist can make a work of art despite the limitations of whatever tool they use (and indeed may get a kick out of doing just that), that doesn't mean the tool can do everything.

I myself am working on a GenAI musical comedy sketch video, but it is only possible with current tech because there's a good opportunity to make cuts every few seconds.
Show HN: Forge, the NoSQL to SQL Compiler
by brady_bastian | 2026-03-09 12:16:22+00:00
https://forge.foxtrotcommunications.net/

I've been a data engineer for years and one thing drove me crazy: every
time we integrated a new API, someone had to manually write SQL to flatten
the JSON into tables. LATERAL FLATTEN for Snowflake, UNNEST for BigQuery,
EXPLODE for Databricks — same logic, different syntax, written from
scratch every time.

Forge takes an OpenAPI spec (or any JSON schema) and automatically:

1. Discovers all fields across all nesting levels
2. Generates dbt models that flatten nested JSON into a star schema
3. Compiles for BigQuery, Snowflake, Databricks, AND Redshift from the
same metadata
4. Runs incrementally — new fields get added via schema evolution,
no rebuilds

The key insight is that JSON-to-table is a compilation problem, not a
query problem. If you know the schema, you can generate all the SQL
mechanically. Forge is essentially a compiler: schema in, warehouse-
specific SQL out.

How it works under the hood:

- An introspection phase scans actual data rows and collects the union
of ALL keys (not just one sample record), so sparse/optional fields
are always discovered
- Each array-of-objects becomes its own child table with a hierarchical
index (idx) linking back to the parent — no manual join keys needed
- Warehouse adapters translate universal metadata into dialect-specific
SQL:
BigQuery: UNNEST(JSON_EXTRACT_ARRAY(...))
Snowflake: LATERAL FLATTEN(input => PARSE_JSON(...))
Databricks: LATERAL VIEW EXPLODE(from_json(...))
Redshift: JSON_PARSE + manual extraction
- dbt handles incremental loads with on_schema_change='append_new_columns'

The full pipeline: Bellows (synthetic data generation from OpenAPI specs)
→ BigQuery staging → Forge (model generation + dbt run) → queryable
tables + dbt docs. There's also Merlin (AI-powered field enrichment
via Gemini) that auto-generates realistic data generators for each
field.

I built this because I watched teams spend weeks writing one-off
FLATTEN queries that broke the moment an API added a field. Every
Snowflake blog post shows you how to parse 3 fields from a known
schema — none of them handle schema evolution, arbitrary nesting depth,
or cross-warehouse portability.

Try it: https://forge.foxtrotcommunications.net

Happy to answer questions about the architecture, the cross-warehouse
compilation approach, or the AI enrichment layer.
by plomme | 2026-03-09 12:16:33+00:00
I don't think anyone objects to curing cancer and better figuring out how our bodies work, but getting into conciousness/ mind uploads/ simulated humans is another can of worms ethically speaking. I'm assuming you've already read the fantastic story about Lena by qntm [1], if not, enjoy some existensial dread.

[1] https://qntm.org/mmacevedo
by vladdoster | 2026-03-09 12:18:03+00:00
Location: Nashville, TN
Remote: true
Willing to relocate: boston || nashville
Technologies: Python, Django, IaC, Terraform, Golang, CI/CD, Azure/AWS, {ba,z}sh, Lua, Springboot,
Redis, Docker, Open Service Broker, {,non}relational & vector dbs, shared services, SOA
Email: mvdoster@gmail.com

resume: https://linkedin.vdoster.com && http://github.com/vladdoster

sr. cloud software engineer @ Mastercard (5 years)

Designed and built Platform-as-a-Service for microservice workloads running on Azure and AWS
to migrate on-prem workloads to the cloud. The platform operates globally, allowing teams to
deploy to several Azure-backed regions worldwide. Helped plan and create regional landing zones
in Azure for application teams wanting to perform globally. Designed platform components using
Terraform, Go, and Node; developed support tooling using Bash, Python, and Azure CLI. De-risk the
platform by addressing security vulnerabilities and ensuring the platform is PCI compliant.

api/devops software engineer @ Harvard Medical School (2 years)

Worked as part of the DevOps team to manage a 15k core CentOS HPC cluster. Designed and implemented
Python-based security scanning pipeline to securely deploy & run Apptainer images via SLURM.
Coordinated cross-team emergency patching eﬀorts. Collaborated with researchers and engineers across
3-letter institutions to deliver secure research infrastructure. Built CI/CD Pipelines with GitHub, Jenkins,
to automate code builds, testing, and deployments. Wrote Puppet modules to manage configuration of HPC fleet.
Wrote custom Puppet module & Python scripts to automatically remediate Tenable findings.

full-stack software engineer @ Global Prior Art (3 years)

Used Django, Celery, Selenium grid, and AWS API Gateway for IP rotation to scrape USPTO patent PDFs,
apply CV for data extraction, and generate reports with relevant claims, saving employees 4-5 hours daily.
Wrote E2E black-box/fuzzing unit tests. Automated on-premise deployment via Github Actions and Ansible.

maintainer @ Zinit (5 years)

Develop and maintain a zsh plugin manager on Github with 4k stars. Implemented unit tests,
containerization, bug fixes, and new features.
See: https://github.com/zdharma-continuum/zinit

contributor @ Open-source documentation (5 years)

Contributed hundreds of documentation corrections and enhancements to 400+ open source projects.
by gwbas1c | 2026-03-09 12:18:07+00:00
Short answer: Skip to 22:45

Long answer: Just jump around. Once you get to the last 1/3rd of the video, there's a lot of close ups of the part of the laserdisc that recorded the credit sequence. The on-screen (laserdisc) text is clearly visible.

The author moves a flashlight around to show how the angle is important; something that won't come across as well in a blog entry.
by zacklee-aud | 2026-03-09 12:19:39+00:00
How does Nvidia's backing of this startup shape competitive dynamics in the AI infrastructure space? If Nscale relies on Nvidia for both capital and GPU supply, does that create an uneven playing field for alternative GPU vendors looking to get their hardware into large multi-tenant AI data centers? Is this $14.6B valuation mostly predicated on continued preferential access to Nvidia's GPU supply, rather than any unique technical or operational advantage that Nscale itself owns?
by darqis | 2026-03-09 12:19:51+00:00
that's incorrect.

They report content they don't agree with. And if the instance owner shared that, the federated user or even instance gets banned.

Most prominent example is the Ukraine.
Even if you post truthfully and in context, that the Ukraine did support the 3rd Reich with link to the Wikipedia article, you get banned, no warning.

So there is political censorship.
And the fediverse is super political.
The 3 big content areas in the Fediverse are politics, LGBT and nudes/nudes drawings including sadly CSAM.

Blocking (and reporting) an instance because of CSAM I can understand. But that too should happen at an individual level.
by btylke | 2026-03-09 12:20:26+00:00
I'm developing a system that uses graph differentials to understand what has changed between library versions and upgrades the target system without breaking things. [0]

Because source isn’t always available, it scans the bytecode of an application and the new library, building a full graph of each component in Neo4j to determine what breaking changes impact the target application. This is then translated into tickets and prompts to drive an LLM to make the appropriate changes.

Handling library upgrades is rarely interesting and just adds to our overall technical debt, so it has been nice to automate it away so that we can focus on features and functionality. It supports Java and .Net currently and we’re actively adding support for other languages.

[0] https://codelogic.com/
Show HN: Local memory layer for AI agents, survives restarts, no embeddings
by JosephjackJR | 2026-03-09 12:20:35+00:00
I got frustrated that every time an AI agent restarts it forgets everything. So I spent six months building a fix.

Synrix is a local-first memory engine for AI agents. It uses a Binary Lattice structure instead of vectors — fixed-size nodes with prefix-semantic addressing. Lookups are O(k) where k is the number of results, not the size of the dataset.
The demo that convinced me this was worth sharing: I told GPT-4 my name, that I like pugs and Ferraris, and a few facts about my project. Restarted the session completely.

The side without Synrix forgot everything. The side with Synrix recalled every single detail instantly. No retraining. No embeddings. No API call. Just prefix lookup in microseconds.
Real numbers from my machine. Direct node lookup 19μs. Prefix queries over 10k nodes 28-80μs. Full agent context restored from cold start under 1ms. WAL recovery tested across 60 crash scenarios with zero data loss. Validated on Jetson Orin Nano at 192ns hot reads.

It runs entirely in-process. No server, no network, no GPU. Works on a factory floor, underground, on a robot that just lost power.
Honest positioning: this is not a vector database replacement. For fuzzy similarity search over unstructured documents Qdrant and Chroma are the right tools. Synrix is specifically for structured agent memory where you control the naming — user preferences, learned facts, session context, task state. You know what you're looking for.
Curious whether anyone has hit this problem in production and how you're currently solving it.

github.com/RYJOX-Technologies/Synrix-Memory-Engine
by jacquesm | 2026-03-09 12:20:49+00:00
Still on time. It is almost two months now and this is such a deep subject and there are so many little tricky bits that I wonder if I will be able to complete the thing but there is still (slow) progress. I never suspected the amount of hard work that goes into building something that is stable at the nano second scale. But I'm becoming more appreciative every day ;)
by incomingpain | 2026-03-09 12:20:52+00:00
AI like any new tool takes time to learn; and its not a weekend. Then while trying to learn, the industry shifts significantly and you have to learn more.

How do you study AI usefulness when you have a serious problem of measuring how skilled they are? What if your study only ever studies people who know essentially nothing about AI? Are your conclusions correct?

I'm only here because:

Quota hit — resets in ~14 hours (around noon tomorrow).

Not yet — resets in ~4h (around 12:20 PM).
by rakag | 2026-03-09 12:21:01+00:00
assessment = "ANALYSIS: "

if any(k in keywords for k in ["strike", "missile", "attack", "bomb", "drone"]):

assessment += f"{random.randint(75, 95)}% probability of kinetic escalation within 24 hours. Recommend immediate asset relocation from projected blast radius."

elif...

Lol.
by hareenklytre1 | 2026-03-09 12:22:24+00:00
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by devnullbrain | 2026-03-09 12:22:25+00:00
A state machine is a perfect example of a case where you would benefit from linear types.

Some things just need precise terminology so humans can communicate about them to humans without ambiguity. It doesn't mean they're inherently complex: the article provides simple definitions. It's the same for most engineering, science and language. One of the most valuable skills I've learned in my career is to differentiate between expressions, statements, items, etc. - how often have you heard that the hardest problem in software development is coordinating with other developers? If you learn proper terminology, you can say exactly what you mean. Simple language doesn't mean more clear.

I wasn't born knowing Rust, I had to learn it. So I'm always surprised by complaints about Rust being too complex directed at the many unremarkable people who have undergone this process without issues. What does it say, really? That you're not as good as them at learning things? In what other context do software people on the internet so freely share self-doubt?

I also wonder about their career plans for the future. LLMs are already capable of understanding these concepts. The tide is rising.
by gwbas1c | 2026-03-09 12:22:58+00:00
> That's the reason why we created programming languages.

No, we created programming languages because when computers were invented:

1: They (computers) were incapable of understanding natural language.

2: Programming languages are easier to use than assembly or writing out machine code by hand.

LLMs are a quite recent invention, and require significantly more computing power than early computers had.
by ahartmetz | 2026-03-09 12:22:58+00:00
> Should the state just prohibit all agreements between two parties unless the state's adjudicator decides they are exactly equal in "power" and permits it?

It's pretty simple. You can write whatever you want into a contract, but if you want to enforce an unreasonable term, you will lose in court and might be forced to remove the term from current and future contracts. That's how it works everywhere. The difference between legislations is just what is considered a reasonable term.