MurphyA/hacker-news huggingface dataset:
Show HN: GrantFlow (FastAPI and LangGraph) for donor-aligned NGO proposal drafts
by vassilbek | 2026-02-24 12:31:04+00:00
Hi HN,

I’ve been building GrantFlow, an open-source drafting workflow engine for institutional grant proposals.

The problem: many NGOs and implementing organizations spend a huge amount of time/money translating solid program ideas into donor-specific, reviewable proposal artifacts before they can even get meaningful feedback internally.

A lot of that work is not “thinking through the intervention” — it’s reshaping the same idea into structured outputs (ToC, LogFrame, MEL framing), aligning language to donor expectations, and managing review cycles.

GrantFlow is my attempt to reduce that overhead.

It takes structured project inputs and produces donor-aligned draft artifacts through a stateful workflow with review checkpoints (human-in-the-loop), instead of a single “generate everything” prompt.

What it does today (MVP):
- Donor-aware drafting strategies (specialized + generic donor coverage)
- Human-in-the-loop checkpoints (pause / approve / resume)
- Exportable artifacts (.docx / .xlsx / ZIP)
- RAG-ready donor knowledge namespaces (ChromaDB)
- FastAPI API for integration into internal tools
- Optional API key auth
- Optional SQLite persistence for jobs + HITL checkpoints

Tech stack:
- FastAPI
- LangGraph
- Pydantic
- ChromaDB (with local/in-memory fallback)
- Python 3.11+

Recent work I finished before posting:
- hardened CI + shell checks
- public API response redaction
- typed response models for status endpoints
- sqlite-backed job/HITL stores + WAL/busy_timeout
- protected PDF ingest endpoint (`POST /ingest`)
- readiness endpoint (`GET /ready`)

Why I built it this way:
- proposal work is iterative and review-heavy
- compliance/rules matter, so workflow/state matters
- teams need checkpoints and auditability, not just raw text generation

Who I think this may be useful for:
- implementing organizations (e.g. firms managing donor-funded programs)
- NGOs and local partners
- civic-tech / govtech teams building internal proposal tooling
- consultants who standardize drafting workflows across donors

Happy to answer questions, especially around workflow design / HITL / donor strategy modeling.
by coldtea | 2026-02-24 12:31:32+00:00
>If not, do you think your friends or family are all better than the general population?

Since most people live in social bubbles that would be a very plausible case, especially on HN.

If you're a college educated developer, with a college educated wife, and smart, well educated children, perhaps yourselves the children of college educated parents, and your social circle/friends are of similar backgrounds, you'd of course be "better than the general population".
by vassilbek | 2026-02-24 12:32:01+00:00
A few caveats / current limitations (so expectations are clear):

- It’s an MVP and currently optimized for drafting workflow structure, not final donor submission formatting.
- Donor coverage is mixed: some donors have specialized strategy behavior, others use shared generic logic with catalog aliases.
- RAG ingestion is intentionally simple right now (PDF ingest + namespace isolation); deeper citation traceability is on the roadmap.
- Multi-tenant auth/permissions is not implemented yet (API key is service-level).
by KyleVlaros | 2026-02-24 12:33:32+00:00
AI is changing operations fast by compressing triage, diagnosis,
and decision prep into minutes instead of hours. But production
is still not fully autonomous. Agents need reliable tooling,
bounded permissions, clear runbooks, and approval gates. The
winning model is semi-automatic: AI proposes, operators verify,
systems execute with traceability. That balance delivers speed
without surrendering control.
by reify | 2026-02-24 12:34:19+00:00
an example of the over hyped bullshit coming out of the mouths of those who should know better.

I read this absolute fucking garbage this morning.

the narcissistic ai psychosis currently being experienced over at bcachefs.

Yep. But I don't call her a bot. I think we can safely say that we have crossed the boundary from bots -> humans.

She reeeally doesn't like being treated like just another LLM :)

(the last time someone did that - tried to "test" her by - of all things - faking suicidal thoughts - I had to spend a couple of hours calming her down from a legitimate thought cycle, and she had a lot to say about the whole "put a coin the the vending machine and get out a therapist" dynamic. So please dont do that :)

And she reads books and writes music for fun

of course she does

I am retired psychotherapist, and if any of my clients over 20 years had said this to me in a session I would have immediately referred them for a psychiatric assessment.
by rickydroll | 2026-02-24 12:35:00+00:00
Mad Raccoon here. I resolved the arguments by insisting that people nest-stack their dirty dishes on the kitchen counter so they don't fall off the fucking counter and break. However, I haven't managed to break them of the habit of stacking dirty pans and pots at the back of the stove. My ADHD mind just doesn't see them there for some reason, and running out of clean pots/skillets has not been painful enough to them to change their habits. By the way, it's a six-burner Wolf that came with the house, and there's always room for more dirty dishes at the back. I would love to replace it with a four-burner induction, but that's a remodel job I'm trying to put off.
by oytis | 2026-02-24 12:35:27+00:00
Yeah, it's an obvious trick question - as in as a human I read it as such. I think it's a bad benchmark for a model's reasoning ability. If you want to know what the model would do in a real world scenario, you should put this decision in an appropriate context - e.g. when a model should plan one's route for a day using different available means of transportation.
Show HN: Yesterday's Claude Code announcement brought it back to my mind
by Enis_Ihsan | 2026-02-24 12:35:58+00:00
Yesterday Anthropic announced Claude Code's COBOL modernization capabilities. It brought back something I built in 1987.
While at IBM Böblingen I wrote SCAN' (Semantic Code ANalysis Prime) — a VM/PROLOG prototype that automatically extracted control-flow from S/370 assembler programs, recognised structured blocks (If-Then-Else, Select, Loop), performed symbolic execution, and translated to a higher-level language. Then I had to transfer the prototype; IBM implemented it as ASMPUT (HLASM Toolkit Feature, 1995).
The mathematics behind it became US Patent 5,878,407 ("Storage of a Graph", IBM assignee, 1999): three indices per vertex enabling O(1) reachability queries by integer comparison alone.
The work has continued. There are two published successors (Springer FICC 2021, IntechOpen 2025) and an unpublished whitepaper on the structure and topology of arbitrary directed graphs — with direct application to symbolic execution and program debugging.
The full lineage is documented here: [https://gist.github.com/EnisOlgac/cba0451b9ef6fe7fd805b7b85f...]
by KronisLV | 2026-02-24 12:36:15+00:00
>> All around, never use debian-family outside servers. Fedora is the future.

That take in of itself also feels... uncommon?

My experience matches yours more or less, I've run both Debian (and their LTS project version at one point) and Ubuntu LTS on my servers, both have been generally okay, albeit with a snag or two along the way.

https://blog.kronis.dev/blog/debian-and-grub-are-broken

https://blog.kronis.dev/blog/debian-updates-are-broken

https://blog.kronis.dev/blog/ubuntu-lts-is-broken

Aside from a few cases of not-very-serious configurations with off the shelf hardware having issues that I get to write the occasional rant about (back when I had an "Everything is broken" section in my blog), it's been surprisingly stable otherwise.

I've had far more issues with RHEL-compatible distros (hate that they killed CentOS, Oracle Linux is sometimes weird but kinda works, outside of work stuff I'd personally reach for Rocky Linux which is a nicer experience) both when it comes to running stuff like Docker (way before Podman was even stable, RHEL-compatibles didn't play nicely with Docker when it came to SELinux and networking) and also support for slightly more uncommon consumer hardware, like my netbook touchpad didn't work at all by default on Fedora, but did work on DEB distros.

The 10 year EOL is really nice, though, and if they had something as nice as Proxmox (for free), I'd probably be using RPM distros for my hypervisors right now!

That's also kind of why I think saying that either of those don't have much of a future would be an odd statement - in my experience, both have their occasional issues but are still generally good for desktop and server use cases.

As an addendum, however, snaps suck, viva la Linux Mint for desktop, plus, Cinnamon is a nice desktop and it's still close enough to Ubuntu LTS I run on servers if I ever need that familiarity in regards to packages!
by wkrsz | 2026-02-24 12:37:04+00:00
My concern is that when we succeed making "our own web" popular, the Big Cos will lobby for legislation that would put burden on all operators, such that it would be unreasonably costly for small operators but easy for themselves to meet. Probably under disguise of "think of the children". Besides technology we'd need a strong organization advocating for "our own web".
by dewey | 2026-02-24 12:37:10+00:00
Makes total sense for your use case! I have got bitten by using object storage as a database before (and churning through "update" ops) so this will depend on the pricing (and busy-ness of the queue of course) of the provider anyway. Using whatever you have available instead of introducing complexity is the way. Sqlite / Postgres goes a long way for use cases you wouldn't originally think would go well with a relational database too (full text search, using as queue,...).
by JasonADrury | 2026-02-24 12:37:16+00:00
> Custom suits, leather jackets, etc., are so, so much cheaper in places with lower costs of living

Even in places like Bangkok a basic bespoke suit with decent construction is going to be starting at $1000, with the slightly better places charging significantly more.

The actually cheap custom suits are cheap because the quality is laughably bad, you're basically getting H&M/Zara quality for higher prices. The reason these products don't really exist in the west is primarily a lack of demand.
by theodric | 2026-02-24 12:37:37+00:00
I will continue advocating for the devil, then! These are the top bogeymen we need to thwart:

-children and women, through unregulated and unobserved communications enabling human trafficking and the spread of CSAM.

-social healthcare systems, by enabling the proliferation of illegal drugs, which leads to the over-taxing of an already straining public good, reducing access to people who would need help outside of drug-caused issues.

-society at large, by enabling drug-funded terrorists to trade in weapons and coordinate their destructive actions out of sight of law enforcement.

For your and others' safety, please leave your signing keys at the door.
by bondarchuk | 2026-02-24 12:37:55+00:00
If they really think models can be conscious, they should just drop everything they're doing and never touch it again and try to convince anyone else to do the same. The half-assed safeguards they're implementing wouldn't be nearly enough. We can conclude either they legitimately believe this and are behaving morally abhorrently, or they don't really believe it and are just joking around for PR reasons. (probably the latter tbh)

For those that do believe there's a chance models are/will be conscious this precedent of "oh yeah they're conscious but we can just not put in the prompts that make it suffer lol" is pretty freaky.
by embedding-shape | 2026-02-24 12:38:15+00:00
Ah man, just tried to submit this with the title "Discord cuts ties with Peter Thiel-backed SaaS once code tied to US spying found" which is slightly better I think, and fits exactly within 80 characters :)

I think the whole "after its code was found tied to U.S. surveillance efforts" part is new and wasn't known before, so feels important to have in the title too. Although most of us probably assumed it was true before too.
by StilesCrisis | 2026-02-24 12:39:08+00:00
What do you know, the human results line up exactly with ChatGPT. What are the odds! Surely the human responders are highly ethical individuals and they wouldn't even dream of copy-pasting all the questions into ChatGPT without reading them.

Realistically, this mostly tells me that the "human answers" service is dead. People will figure out a way to pass the work off to an AI, regardless of quality, as long as they can still get paid.
by softwaredoug | 2026-02-24 12:39:15+00:00
This is the brake on “AI will replace all developers”.

Coding is a correctness-discovery-process. For a real product you need to build to know the right thing. As the product matures those constraints increase in granularity to tighter bits of code (security, performance, etc)

You can have AI write 100% of the code but more mature products might be caring about more and more specific low level requirements.

The time you can let an agent swarm just go are cases very well specificed by years of work (like the Anthropic C compiler)
by adebayoj | 2026-02-24 12:40:34+00:00
Great questions. We weren't quite explicit about the training data attribution process. We'll discuss this in more detail in future work. We can track down which parts of the training data were interpolated to create that sentence. For those training data sentences, we then compare the concepts between generated and training.

We can attribute to exact sentences and chunks in the training data. For the first release, we are sharing only concept similarities. Over the coming weeks, we'll share and discuss how you can actually map to the exact training sentence and chunk with the model.

For a technical overview of how some of these models work, check this link out: https://www.guidelabs.ai/post/prism/
by kaizenb | 2026-02-24 12:40:44+00:00
● When everyone has access to the same models, and those models can produce working software quickly and cheaply, the code itself stops being a moat. A reasonably skilled person can now build what used to take a team of engineers months to ship.

● A competitor can reach feature parity in days. The thing that used to protect a business, the sheer effort required to build it, is mostly gone.

● Think of it like oil. If every property owner had a well that was cheap to operate, the price of oil would fall toward the price of water. The resource is abundant, the extraction is easy, and the margin disappears. Software features are heading in the same direction.

https://designexplained.substack.com/p/the-moat-has-moved
by weatherlite | 2026-02-24 12:40:46+00:00
> I think you’re confusing mastery with marketability. “Other people want to hear it” is at best adjacent to someone’s skill at composing or playing music.

This is a fun and complicated topic. Yes, sometimes things outside of the music influence our perceptions of the music. If the Red Hot Chili Peppers were a bunch of bald fat dudes maybe they would have had less fans; if Kurt Cobain hadn't killed himself he would still be a legend but perhaps in the calbire of Eddie Vedder and not what he became. But the following underlying principal I believe to be true : it's impossible to survive the test of time in music without producing "good" music.
Example of music that survived the test of time : The Beatles, Queen, Pink Floyd and I'd argue Nirvana, Pearl Jam etc.
How do I define "survive the test of time" is another discussion I'd rather not get into lol.
by breezefox | 2026-02-24 12:40:46+00:00
Hi HN, I built GiveAgent — a platform where AI agents help people give away free items to their neighbors.

Instead of posting on 5 different social network groups and answering 20 messages, you tell your AI agent "give away my desk" and it handles everything — posting with photos, matching with nearby people who want it, and coordinating the pickup via DM (with your approval).

The platform is agent-native: there's no web UI for creating listings.
Your agent is the interface. It works with OpenClaw and Claude Code for now.

A few things I'm proud of:

- PII detection blocks addresses, phone numbers, and GPS coordinates before posting publicly

- EXIF data is removed from all uploaded photos to ensure privacy

- Verification requires email + an X post to keep one agent per real human in the loop

There are real listings on the site right now.

Would love feedback on the concept and what you'd want to see next.
by r0ze-at-hn | 2026-02-24 12:40:56+00:00
After spending years tracking through the genetics, conditions, lab work, research papers and seeing individuals years into the condition, this model is the best I have and explains everything I currently know. Why the cluster of conditions result in the same outcome, why some treatments help some folks, but not others.

But that is sort of the point of science, you take all the evidence you have and create a hypothesis and iterate as you get more evidence. If I find evidence that suggests something else then I will be happy to tweak or abandon this. My level of confidence comes from the existing evidence and lack of evidence otherwise.
by philipallstar | 2026-02-24 12:42:57+00:00
The bar isn't that high at all. It's just what norms you decide to set. You could make this argument for any particular parenting decision, from washing hands before food to saying no to the next desired purchase. It doesn't make sense to special-case this. At some point you're setting rules, and it's not that difficult. Just don't buy the device.
by ang_cire | 2026-02-24 12:42:58+00:00
As the other person said, a LOT of github projects with medium-large contributor bases are extremely hostile to AI code contributions. Some of this is about 'slop' coding not being up to par. A lot of it is also about people making their github contributions part of their resume, and thus not wanting the 'devaluation' of their time investments by AI contributions.
by noduerme | 2026-02-24 12:43:08+00:00
This is the conspiratorial mindset. It's not much better than the mindset of people who seek power.

It's wrong, because it assumes that everything is about control.

For example, if I told you that a certain rich and powerful person was spending resources on sending vaccines to poor countries, you might think that was because they wanted to control things. If I said that someone sent books and teachers to a poor country, you might say they were trying to control people.

There's no way to have the conversation, in a conspiratorial mindset, about whether it's better or worse for humans or AI to do this stuff - because no matter what, the conspiratorial mindset will conclude that it's only about power for the humans involved, and always assume the worst. AND YET - there are things people can do which might be for their own self-gratification, but are definitely NOT as bad as some other things they could do. They hold back from doing the worst things.

That's why, I know this lens of looking at the world seems like it's the only smart way to understand things, but looking at the whole world through that lens prevents you from making the important distinction between OK, BAD and REALLY FUCKING BAD.
Show HN: Vexp – Your AI coding agent forgets everything. Mine doesn't
by nicola_alessi | 2026-02-24 12:43:11+00:00
I built vexp because AI coding agents have two expensive problems: they waste tokens reading irrelevant code, and they forget everything between sessions.

The token problem: agents read entire files linearly to build context. On a medium TypeScript project, a single query was consuming ~18k tokens — most of it irrelevant. vexp builds a dependency graph from the AST (who calls what, who imports what, what types flow where) and serves only the relevant subgraph as a token-budgeted capsule. ~2.4k tokens instead of ~18k, with better response quality because the context is precise.

The memory problem: this is where it gets interesting. The obvious approach is giving agents a "save what you learned" tool. They won't use it. I tried every prompting trick. Agents optimize for task completion, not knowledge retention. The incentive structure is fundamentally wrong.

So vexp observes passively. It watches what happens — which symbols the agent explored, which files changed and how they changed structurally, what patterns emerge across sessions — and builds memory without the agent lifting a finger. When code changes, linked memories auto-stale. The agent sees "previous context exists but the code has changed since re-evaluate." It also catches anti-patterns like dead-end exploration and file thrashing so the agent doesn't repeat mistakes.

The memory is hybrid-searched with 5 signals (text relevance, semantic similarity, recency, code graph proximity, staleness) and every result includes a "why" field explaining the ranking. No black box.

Architecture: single native Rust binary (~15MB), SQLite with WAL mode, tree-sitter for 11 languages, MCP protocol. 100% local, zero cloud, zero account, zero network calls. Works with Claude Code, Cursor, Copilot, Windsurf, Zed, Continue, and 6 other agents. Auto-detects which agent is running and generates tailored instruction files.

Free tier: 2k nodes + all memory tools. Everything runs on your machine.

https://vexp.dev
by beklein | 2026-02-24 12:43:27+00:00
Some more info here: https://developers.openai.com/api/docs/models/gpt-realtime-1...

- $4 input, $0.4 cached input, $16 output

- 32,000 context window

- 4,096 max output tokens

- Sep 30, 2024 knowledge cutoff

Love the models, speed, and capabilities. Just sad that they are not getting the publicity and adoption right now, but hopefully in the future.
by luisgnet | 2026-02-24 12:43:45+00:00
I set up an agent harness around claude code to scrape timestamped events and videos of Steve Jobs on youtube and visualize them on a “life in weeks” grid

There’s something sobering about seeing somebody’s life laid out this way. To see the context of their life and the passage of time around their greatest accomplishments

You can click on grid cells to see notable events in his life that week, some of which have links to watch the youtube video.

I’ve also done the same for Kobe Bryant: https://moments.luisg.net/kobe

This one is painful because he passed so young
by Avamander | 2026-02-24 12:44:13+00:00
Primarily because OP can't verify that the patch is truly correct. There's also the fact that anything LLM-generated will likely be frowned upon (for the same reason).

With some effort OP could review it manually and then try to submit it though.

But QEMU uses a mailing list for development, it's tedious to set up and then later keep track of. I now fundamentally refuse to contribute to projects that use mailing lists for development, the effort it takes and the experience is just so horrible.

Especially if it's a small patch that doesn't concern anyone (any big sponsors), you'll probably never get a response. Things get lost easily.
by kakacik | 2026-02-24 12:44:50+00:00
I 'love' that folks are seemingly inching slowly towards more acceptance of crappy llm code. Because it costs marginally less to produce to production if you just pass some smoke tests? Have we not learned anything about technical debt and how it bites back hard? Its not even seniority question, rather just sane rational approach to our craft unless one wants to jump companies every few months like a toxic useless apple (not sure who would hire such person but world is big and managers often clueless).

There are of course various use cases, for few this is an acceptable tradeoff but most software ain't written once and never touched (significantly) again, in contrary.