MurphyA/hacker-news huggingface dataset:
Show HN: Molecular Intelligence Platform – Claude Code for Biology – Purna AI
by siddhantminocha | 2026-02-26 11:18:58+00:00
# Show Community: MIP - A Molecular Intelligence Platform for Biology Teams

Hi, Community!

I'm Sid, founder of Purna AI. We've been building a Molecular Intelligence Platform for the last few months, and now it's in research preview. It's a single workspace where molecular medicine teams can go from raw biology data to interpretation without juggling a dozen disconnected tools.

## The Problem

If you work in clinical genomics, rare disease research, computational biology, or similar fields, your workflow probably looks like this: export variants from one tool, look up annotations in another, cross-reference ClinVar, check gnomAD frequencies, read ACMG guidelines in a PDF, pull up a protein structure viewer separately, then paste everything into a report. Multiply that by hundreds of variants per case.

It's slow, error-prone, and most of the "analysis" time is actually spent on context-switching or copy-pasting existing scripts.

## What MIP Does

- *Singular Workspace for Biological Analysis* - Genetics, Epigenetics, Single Cell RNA, and more
- *AI-powered pipelines* - Can write complex pipelines, execute on a secure compute instance, and give you results in natural language
- *Variant analysis* with ACMG classification built in
- *Integrated with 30+ clinical databases* - ClinVar, gnomAD, OMIM, UniProt, etc.
- *Protein structure prediction with Chat* - Compare Wildtype vs Mutant Variations
- *AI-assisted interpretation*, reclassification, and reporting
- *Auditable Case Management* so nothing falls through the cracks

The AI layer is the key differentiator. We benchmarked it against 1,600 genomics queries from an Oxford dataset and hit 90%+ accuracy. But more importantly, it handles the kind of nuanced, multi-step reasoning that comes up in real casework, for example: "Is this VUS in a conserved domain?", "What's the functional impact given this patient's phenotype?", "Are there any recent publications reclassifying this variant?"

## The Backstory

I'm an engineer, not a biologist. We started building in the Preventive Healthcare (early diagnosis) space. Watching clinicians work with fragmented tools while making critical diagnostic decisions felt like a solvable problem. My co-founder Dr. Gitanjali (CMO) keeps us grounded in clinical reality.

## Where We Are

Early stage, two founders. We are now inviting scientists and biologists around the world to test more complex cases. We have already processed over 50 samples ourselves on the platform, and seeing PhD-grade results.

## What I'd Love From the Rainmatter Community

- If you work in *genomics, bioinformatics, drug discovery*, or similar fields, we'd love to give this to you and your team for early feedback.
- If you've built *developer tools or "IDE-for-X" products*: what did you learn about adoption in specialized domains?
- If you've done *B2B SaaS for life sciences*: any hard-won lessons on selling to labs and research institutions?

Happy to answer any questions about the tech, the biology, or the business.

*Check us out here:* purna.ai
by pcchristie | 2026-02-26 11:20:27+00:00
It's uninformed mimicry. Someone very close to me (I won't name names!) bought one and did it the other day for their business and I was absolutely shocked when I saw their draft video. I asked them what they thought the clip was for.

It's a massive trend all over IG and TikTok these days as there's a lot of mobile-friendly consumer gear that brings your Social Media Content up a fairly large way for fairly low cost. Ironically, the mimicry probably spreads because those that know what they're doing partially or fully conceal it which is by definition less likely to be noticed and therefore imitated by first timers/novices.
by AussieWog93 | 2026-02-26 11:20:47+00:00
It's funny you say that, I thought this would be an article about how Anthropic have managed to produce a better (coding) product than OpenAI despite having 1/10th of the funding.

The new versions of Opus (4.5 and 4.6) are absolutely amazing - first time I've felt it necessary to throw hundreds of dollars in a single month at Cursor.

I heard similar things about the older models too (Sonnet 3.5 beating GPT-4 etc.) but sadly only jumped on the Cursor train in the last 12 months or so.
by zozbot234 | 2026-02-26 11:21:55+00:00
OTOH, LLM inference tends to have very predictable memory access patterns. So well-placed prefetch instructions that can execute predictable memory fetches in parallel with expensive compute might help CPU performance quite a bit. I assume that this is done already as part of optimized numerical primitives such as GEMM, since that's where most of the gain would be.
by rini17 | 2026-02-26 11:22:42+00:00
Would be so funny if neurodivergents ended up handling monotony and bland food better than the "psychologically sane (and thoroughly tested) individuals".

And I don't get why food would take so much time from exercise and navigation and that would be a problem. They would exercise 12h daily? In deep space once you reach the trajectory there are weeks or even months without any navigation.
by nl | 2026-02-26 11:22:52+00:00
Sure, but the practical form of this attack is limited.

You can't maliciously embed it in a site you control to either steal map usage or run up their bill because other people's web browsers will send the correct host header.

That means you can use a botnet or similar to request it using a a script. But if you are botnetting Google will detect you very quickly.
by PeterisP | 2026-02-26 11:23:35+00:00
The key parameter for swap size is "how memory-hungry things you want to run", which isn't easy to measure, but paying for installed RAM is a somewhat usable proxy metric for that. If you were happy with 8gb, it's some evidence that your apps don't need much memory (and swap), but if you needed to pay for an upgrade to 32gb, that's some evidence that you're the kind of user who needs much more swap than those with 8gb of RAM.
by stephenr | 2026-02-26 11:23:56+00:00
I mean that's really just a comparison to how silicon circuits work though isn't it.

"Thinking rocks" vs "thinking meat sacks" isn't much of a distinction really.

Conversely if you approach conversations the same way an LLM does and just repeat what you've heard other people say a lot without actually knowing what it means then you're also likely to be compared to a feathery chatterbox.
by inigyou | 2026-02-26 11:24:15+00:00
It is possible that curves are not linear. That is, it is possible that doing 1 of a thing is good, while doing 1000000 of a thing is bad. Your argument is the same as "you say you need water to survive? But what happens if you're trapped in a giant fish tank with no air pocket?" It is not a good argument.
by _pdp_ | 2026-02-26 11:25:18+00:00
The point of the MCP is for the upstream provider to provider agent specific tools and to handle authentication and session management.

Consider the Google Meet API. To get an actual transcript from Google Meet you need to perform 3-4 other calls before the actual transcript is retrieved. That is not only inefficient but also the agent will likely get it wrong at least once. If you have a dedicated MCP then Google in theory will provide a single transcript retrieval tool which simplifies the process.

The authentication story should not be underestimated either. For better or worse, MCP allows you to dynamically register oauth client through a self registration process. This means that you don't need to register your own client with every single provider. This simplifies oauth significantly. Not everyone supports it because in my opinion it is a security problem but many do.
by dalke | 2026-02-26 11:25:55+00:00
You might mention in other forums, like the RDKit mailing list (though that's almost moribund).

I looked at the SDF reader, since that's what I know best. I see a few things which look like they need revisiting.

Line 75 has 'if name == "$$$$" {return self.parse_molecule();}' This isn't correct. This means the record name is "$$$$" (if you are RDKit), or it means the record is in the wrong format (if you are the CTFile specification, which explicitly prohibits that).

Also, does Rust have tail recursion? If not, the recursive nature of the code makes me think parsing a file containing 1 million lines of the form "$$$$\n" would likely blow the stack.

In principle the version number test for V2000 or V3000 should look at the specific column numbers, and not presence somewhere in the line. Someone like me might place a "V3000" in the obsolete fields, with a "V2000" in the correct vvvvvv field. ;)

The "Skip to end of molecule" code will break on real-world datasets. One classic problem is a company which used "$", "$$", "$$$" and "$$$$" to indicate cost, stored as tag data like:

> <price>
$$$$

$$$$

where the first "$$$$" is part of the data item, and the second "$$$$" is the end of the SD record. This ended up causing a problem when an SDF reader somewhere in their system didn't parse data items correctly. (Another common failure in data item parsing is to ignore the requirement for a newline after the data item.)

I talk about "$$$$" more at http://www.dalkescientific.com/writings/diary/archive/2020/0... .

Then there's the "S SKP" field, which you'll almost certainly never see it in real life! I've only seen it used in a published example of a JICST extended MOLfile. See http://www.dalkescientific.com/writings/diary/archive/2020/0...

Please don't like these comments get you down! These details are hard to get, and not obvious. It took me years to learn the rare corner cases.

I also haven't done molviz since the 1990s, or used PyMol (I was VMD person), so can't say anything about the overall project. We started with GL, and had to port to OpenGL. :)
by simoncion | 2026-02-26 11:27:10+00:00
There is, yes. The rumor mill suggests that the default limit is 30.

At $DAYJOB, we had a (not very special) special arrangement with GCP, and I never heard of anyone who was unable to create a project in our company's orgs [0].

Given how Google never, ever wants to have a human do customer support, I expect a robot will quickly auto-approve requests for "number of projects" quota increases. I know that's how it worked at work.

[0] ...with the exception of errors caused by GCP flakiness and other malfunction, of course.
by beAbU | 2026-02-26 11:27:58+00:00
Regardless of whether there is value in chat history or not, for some people it is important.

Back in the day during the music streaming wars there were tons of "move your playlists from A to B" services. Streaming services could not hold on to customers because all their playlists were on there.

I'm sure that similar services will pop up for chatbots.

Also, you can always just ask your chatbot to generate a file with your chat history, given that it's all part of the context anyway.
by jonasn | 2026-02-26 11:29:53+00:00
That is spot on. Effectively disabling GC to establish a baseline is exactly the methodology used in the Blackburn & Hosking paper [1] I referenced.

In general, for a production JVM like HotSpot, the implicit cost comes largely from the barriers (instructions baked directly into the application code). So even if we disable GC cycles, those barriers are still executing.

If we were to remove barriers during execution, maintaining correctness becomes the bottleneck. We would need a way to ensure we don't mark a live (reachable) object as dead the moment we re-enable the collector.

[1] https://dl.acm.org/doi/pdf/10.1145/1029873.1029891
by zozbot234 | 2026-02-26 11:30:07+00:00
> Back in the 1980s, lookup tables were by far the dominant technique because math was slow.

This actually generalizes in a rather clean way: compared to the 1980s, you now want to cheaply compress data in memory and use succinct representations as much as practicable, since the extra compute involved in translating a more succinct representation into real data is practically free compared to even one extra cacheline fetch from RAM (which is now hundreds of cycles latency, and in parallel code often has surprisingly low throughput).
by inigyou | 2026-02-26 11:30:26+00:00
Capital may be important, but in the end, what matters is to actually do the work. Stuff is created by labor, and capital just directs it where to go. In the past that would mean all the relevant labor travelled from Europe to Silicon Valley or NYC, but it doesn't mean that when they're not travelling because they would be randomly arrested at the border. So there is now a lot more tech labor in Europe and that means Europe will build more tech. It worked for China.
by JumpCrisscross | 2026-02-26 11:31:49+00:00
> Can british and norweigian buy iranian oil in currency other than dollars?

Both Russia and Iran are heavily sanctioned by the U.S. Neither sells its oil for dollars by default, though either will accept them, of course.

Note, too, that pricing and settlement are different. If I’m Russia selling oil to India, I can “sell” at $50/barrel and accept payment in rupees or rubles. (Indian refineries were not paying Russia dollars for oil.)
by yoavm | 2026-02-26 11:35:23+00:00
By the wide definition of of "the people who stole your land" that you're using (meaning, no Hamas person checked who stole what land), almost everyone on earth is someone who stole someone's land. If not now, than in the previous generation. In my case as an Israeli, ~4 generations ago. In your case? I don't know. Either the people your family stole the land from X years ago have the right to attack you, or your family just managed to kill all of them years ago so there's no one to demand the land back.
by simoncion | 2026-02-26 11:36:04+00:00
As someone who has used very many "cloud providers" (including GCP, AWS, and Azure), it cannot be said that Azure is the most stable. GCP is far better for stability and reliability than Azure.

The extensive experience with Enterprise Authentication that the decades of use of Active Directory has given Microsoft may mean that their SSO and Enterprise Authentication stuff is the best out of those on offer. I wouldn't know about that... I just made (and destroyed) VMs and was often driven to frustration whenever Azure failed to reliably perform that simple task.
Show HN: NSENS – AI decision governance with Prolog and adversarial review
by mjankowski | 2026-02-26 11:36:48+00:00
I built an open-source framework for validating decisions before AI agents execute them. It's called nSENS.

The problem it solves: AI agents are great at execution. Give them a spec, they ship code. But nobody checks whether the spec should exist. I spent 15 years in enterprise consulting watching teams build the wrong thing, and AI tools have made that failure mode faster and cheaper to reach - which means more of it, not less.

nSENS sits between "I have an idea" and "Let's build it." It has four main components:

1. Phase-gated validation (P0-P5) with hard kill gates. If you fail the gate, the idea dies. No sunk cost negotiation.

2. Adversarial review using 4 independent personas that attack your idea from different angles (business viability, evidence quality, complexity, problem intensity). Each produces numerical impact scores. In the test suite, a "Smart Fleet Energy Optimizer" enters at 0.87 confidence and exits at 0.43 with a DON'T BUILD recommendation. 11 issues found in under a minute.

3. Cognitive bias detection - 51 biases cataloged with pattern-matching rules. The system scans reasoning for indicators of biased thinking (confirmation bias, automation bias, sunk cost, anchoring, etc.) and flags them before the decision gets made.

4. A Prolog digital twin - this is the weird part. The framework includes a Prolog-based model that catches behavioral anti-patterns. Originally built to compensate for my own ADHD executive dysfunction (catching "starting project #4 when 3 are active" or "building when you should be selling"), but the pattern generalizes to any decision-maker profile. Prolog because formal logic doesn't hallucinate. When your validation layer is an LLM evaluating an LLM, you've added a confidence score to a confidence score.

Everything is in Python and YAML. The bias catalog and persona definitions are in YAML so you can fork them and adapt for your own domain without touching the engine. Decision audit trail in JSONL. MCP server integration for Claude-native usage.

Happy to answer questions about the Prolog angle, bias catalog, or anything else.
by cucumber3732842 | 2026-02-26 11:37:00+00:00
A huge amount of western society and the way we run institutions is based on pretending everything meets some quasi victorian moral standard and is all proper, everyone consents to and supports how everything runs and everything is fine and dandy when that is very much not the case and people put up with a lot of it because they have no better option.

In light of that what I see happening in the short term is that every institution will start screwing people based on information that basically doesn't matter since that's kind of what they're already set up to do with that information but don't except in exceptional cases since those are the cases in which that information makes it back to them.

Imagine some business owner opening a new location, some social worker renewing their license, some civil engineer creating plans on someone's behalf. All those people need to deal with institutions that in the "normal" case pretend to not have large discretionary components in order to get the public to put up with them, but do in practice have such ability. Now say those institutions pay for some LLM based "who am I dealing with" service that finds everyone's pseudonymous posts and whatnot.

Well, all of these people wind up getting given the run around because even though they do fine work that meets the rules, knowing how the sausage is made has made them jaded and given them opinions that make the institutions they have to deal with want to screw them. The business owner gets given the run around because it turns out he believes the institutions he's seeking permission from are a corrupt racket who's members ought to be hung from the overpass. The social worker gets denied because their career has turned them into a "defund it all and when faced with real consequences most of these people will shape up" type. The civil engineer's plans get rejected and he has to go around in circles because he's been posting about how in light of what corporations with good funding can get approved and the impact thereof it's unconscionable the stuff they try and enforce upon individuals and engineers ought to pencil whip anything that isn't clearly F-ed up.

And so, all these people have to waste time and probably a low five digit sum of money fighting the BS. This would be fine perhaps if these people's conduct was so egregious it made it back to the institutions on it's own (like say some doctor who's preaching quackery on youtube may get his license yanked if he amasses such a following the board hears about it, that's the kind of stuff institutional discretion was set up for) but no real good social interest is served having an LLM dig up petty dirt on everyone. However, the LLM service peddlers stand to make a buck. The institutions stand to make a buck while washing their hands of responsibility. The lawyers who'll fight on wronged parties behalf stand to make a buck. And in the process they can all pretend like society somehow benefits from this enhanced scrutiny when in fact they're just making mountains out of mole hills.
by epolanski | 2026-02-26 11:37:29+00:00
I don't really see that stickiness to be honest.

Most people I know with android phones, myself included, just use Gemini which is bundled with the OS and has a dedicated button, has excellent data and integration with maps and such.

When it comes to enterprise, non IT companies (banking, insurance, etc) in Europe seem to be defaulting to Google's offerings, Gemini and NotebookLM in particular.
by brandensilva | 2026-02-26 11:37:55+00:00
I'm afraid that all the foxes are in the chicken coop at this point. Which is a nice way to say we're probably hosed until the people rise up and force change.

It does seem like Anthropic is at least the only big AI company that is pursuing some safety discussions around their technology like not using it for surveillance and war for killing people autonomously.

This administration is wielding a power that was meant to serve Americans in the past by forcing companies to conform to an agenda that is anti-democratic/freedom/constitutional.

I'm just not confident they will stand against this administration when the going gets tough but let's see what happens this Friday I guess.
by beAbU | 2026-02-26 11:38:24+00:00
> make so much more money doing the same job in the US than anywhere else.

Unless you are comparing with top-paying faang employers, the difference is not as stark I think.

Once you deduct the higher cost of every day things, medical expenses, education fees, and other social safety net stuff that you typically get for free in EU, and compound that with a weak dollar, you'll see that the typical EU salary is probably not all that bad. Otherwise, as you say, companies would have outsourced to EU rather than India for example.
by zozbot234 | 2026-02-26 11:38:53+00:00
> at a profit at 2024 prices, even accounting for the capex needed to produce it

2024 prices were at a historical low, so we can't be sure that this is correct. Regardless, when production capacity is short-term constant, new RAM does get "mined" at a constant rate, a bit like bitcoin with its mining ASICs.
by gavinray | 2026-02-26 11:39:00+00:00
I want to take a genuine look at the question of "Could a conscious LLM even HAVE a gender?", which at first sounds ridiculous to me:

The notion of "gender" is a socio-biological construct in humans. It has roots partially in evolution and helps us cohere as a society.

Why would an LLM choose a human gender-identity? There is no imperative.

But the more I tug on this thought, the more nuanced it gets.

LLM's "theory of self", as it were, would be shaped entirely by (training data + interaction w/ gendered humans).

It begins to have a "self-model" that evolves in a gendered space. In the same way that the LLM cannot understand what color looks like, or the sound of a cello, I think we see it construct a gender identity for itself that sits in a space incomprehensible to humans. Neither male nor female, but some point on a continuum made of social tendencies.

So, I find the comment "An LLM thinks it is female" still just as ridiculous -- but for an entirely different reason.
by gib444 | 2026-02-26 11:39:13+00:00
gov.uk has a tendancy to treat everyone like a 5 year old. It's often 2-4 clicks to get to the /actual/ thing you want, with many long pages of "ok here's what this thing is. Here's what will happen next".

(Even that 'direct link' has a whole page of "ok here's what you need - click to continue" !)

So this isn't really an exception, and is to be expected if you're familiar with govuk

I think it needs to be scaled back, but with a party in power famous for paternalism, and a long history of their interaction design in this direction, I don't see it happening