Show HN: SlickFast Deterministic Chart/Dash Renderer, No Browser(JSON → SVG/PNG). SlickFast started 6 months ago, and evolved in a super backwards way. I was using lowfruits to look for good SEO keywords, I wanted to make a simple free tool to rank with SEO. I found some great keywords related to graphs/charts. I did research for optimizing for SEO. Turns out edge processing / using static HTML is super fast and lightweight, great for SEO. I made freepiechartmaker.com. I was really blown away by how fast the site was loading, and how lightweight all the processing was. The site renders changes on the fly, and is much much faster than other sites in the space. I started looking into the tech, pure math rendering, and I saw a lot of openings for what this tech can do. That's how SlickFast was made.
SlickFast is a JSON input > pure javascript SVG Native render core > with PNG+SVG output. No headless chrome. No library calls. deterministic output. on my local machine(m1 max) it renders 140,000 svg charts a second. PNG @ ~50/sec at retina (scale-2) and ~145/sec at scale-1, single-core. (we have a benchmark mjs in our github repo)
SlickFast can render 47 chart types. Any resolution, aspect ratio, any tile combination, easily deliverable anywhere, and made for agentic workflows.
Our MCP is free and open source.
Tiling allows complex dashboard creation. Each dashboard is only one render. SlickFast API serves URL endpoints. SlickFast is lightweight, we have a hero dashboard demo at slickfast.com/deck that is updating every 3 seconds. This is millions of renders, and the cost per month is less than $10.
SlickFast was intended to be ready for agentic workflows, and visualizing agentic output as cheap and frictionless as possible.
A benefit to the SlickFast deterministic pure math render core, is that edge servers will easily cache images. Millions of people can see a SlickFast endpoint, and only a few renders will actually be used, because the edge servers will happily cache our very small charts.
SVG Native chart rendering provides for retina quality, at 50kb image sizes.
Our favorite demo is LIVE GitHub dashboards, living right on the repo page. just put a SlickFast URL in your readme. You can see our living dashboard at github.com/slickfast/slickfast. We have made githubs templates for the public. you can easily add your own dashboard. https://github.com/SlickFast/github-dashboard-template
SlickFast also has telegram deliverable templates. https://github.com/SlickFast/telegram-weather-template - if you go to the repo, the weather dash being sent via telegram, is on the repo as a live dashboard - Its updated everyday with a real weather report.
Another great demo is our X.com NYC air quality index bot (@NYCAIRREPORT). Slickfast posts a AQI dash on X.com 3 times a day. With the new x API terms, each post is only about 2 cents. The cost on the slickfast side is basically nothing. Our free API tier currently provides 250 renders a month. Enough space for a few bots. Because slickfast was made for agentic use, an agentic was able to design the entire dash and inputs in only a few takes. AI Agents, can nearly one shot most tasks. One or two minor revisions is usually all it takes.
Slickfast.com Github.com/slickfast/slickfast
Slickfast is free and open source under AGPL license. I'm so delighted to provide some real value for free. We have a roadmap with some super cool things coming. Slickfast is very powerful for email, or tying to APIs.
Thanks Everyone! I would love to hear your thoughts.


TIME Is Serving AI Bots a Different Website, with Ads Built In.


What Is Decispher?. What is Decispher?
The decision layer between your team and every AI agent
Decispher is the system of record for engineering decisions. It automatically captures the decisions, conventions, constraints, and rationales your team produces every day from Slack, GitHub PRs, and docs, then serves that knowledge to both humans and AI agents the moment they need it.
Think of it as a senior engineer who has read every Slack message, every PR, and every architecture discussion your team has ever had, and is always available to answer "why did we do this?" with a cited, accurate answer in under a second.
decispher.com


Show HN: Rextio – Compile Python code natively using Rust with CPython fallbacks. Hi, HN! Let me introduce my experimental project Rextio.
While Python is widely used in fields such as AI and data analysis and offers the advantage of fast coding, it often causes some performance issues because Python is an interpreter language.
Of course, there are various alternatives to address Python's speed issues. In some cases, Python code is used only for initial prototypes, and the development is re-evaluated in Rust or C/C++ upon transitioning to production.
However, I'm experimentally developing this tool called Rextio as a new alternative to solve speed problems without abandoning Python.
Rextio improves performance by automatically converting parts of Python hot paths that can be compiled natively (such as type-hinted static code) into Rust code and compiling them into binaries. It then falls back to the rest of the code as is and executes it using CPython. This allows for the complete preservation of the original Python logic while aiming to improve execution speed for specific parts.
Roughly speaking, it is as follows:
```
typed Python project<p>-> analyze supported native candidates<p>-> reject unsafe or unsupported functions<p>-> generate Rust + PyO3 for accepted functions<p>-> generate Python fallback wrappers for the rest<p>-> build import-compatible artifacts
```
Please refer to the documentations in the Git repository for details.
Performance improvements are also confirmed when comparing execution with the native binary converted to Rextio versus execution with the original Python code only using CPython.
* When running basic Python code converted to native: 57.73×
* When running NumPy converted to native: 2.52×
* When running NetworkX' Dijkstra algorithm converted to native: 3.68×
* When running `pandas`' `Series.map` converted to native: 66.14×
* When running PyTorch deep MLP converted to native: 1.02× (Speed   improvement limited due to using C extensions in existing Python packages)
* When running TensorFlow eager chain converted to native: 1.04× (Speed   improvement limited due to using C extensions in existing Python packages)
Please refer to the [`rextio-benchmark` Git repository](https://github.com/rextio/rextio-benchmark) for the source code and execution environment information used in the benchmarks that yielded the above results.
Rextio has a Core CLI that converts and compiles Python code, and we are developing additional plugins that define rules for converting individual Python packages into Rust code to be attached to it. We have currently developed some plugins for NumPy and others (`rextio-numpy`, `rextio-networkx`, `rextio-pandas`, `rextio-torch`, `rextio-tensorflow`, etc.), and the benchmark results above were obtained using these plugins.
Rextio is still an early-stage project with much room for improvement, and compiling Python code into Rust-based native code does not guarantee performance gains in all situations.
For instance, if there is frequent data exchange between natively compiled parts and CPython fallbacks, data conversions crossing the boundaries can actually lead to a decrease in speed. However, in such cases, Rextio tries to fall back the entire process to CPython without passing through native code to prevent this degradation.
If you have any questions or feedback, I'll do my best to answer them and incorporate them into feature improvements.
Thank you.


Ask HN: How are you using AI to learn?. It's time to start learning advanced topics again and I thought it'd be at least interesting to take advantage of LLM's to help me learn better or faster.
I've read here that some of you are using "socratic skills" where you give the LLM some content and then you get a series of questions in order to learn but I don't think it's the only approach to this.
I'll start tackling the "Attention is all you need" paper and then I want to go deeper.
What's your approach?


AI Startup Cognition in New Funding Talks at $40B Value.


A YC Startup That Wants to Kill Tokenmaxxing Just Raised $13.5M.


Ex-DeepMind David Silver Raises $1.1B for AI Startup Ineffable.


The global edtech boom is fading as investors look elsewhere.


Ask HN: Will we ever do something about patent trolls?. Some of you may have seen a project like Mycroft AI (nothing to do with LLMs!) from back in around 2017 or so, it was an open source Alexa alternative, even had skills and everything. I kept wondering why it quietly disappeared, and the repo was archived. Turns out a patent troll hit them hard, drained them of all their funds in court, till they shut down.
https://patentprogress.org/2024/05/another-startup-bites-the-dust-courtesy-of-patent-trolls/
https://www.theregister.com/2023/02/13/linux_ai_assistant_killed_off/
Then there's many others, and I'm sure I'm missing some, but here's a list of companies / projects hit by patent trolls with various outcomes:
That time some company shut down a free / open source site with a patent that came 10 years after the technology already existed.
https://www.techdirt.com/2014/11/19/patent-troll-kills-open-source-project-speeding-up-computation-erasure-codes/
https://x.com/JamesBessen/status/532906754364149760
Here's one where the courts did the right thing for once, against the Gnome project (note Gnome probably has better funding than most of these other smaller projects).
https://opensource.org/blog/gnome-patent-troll-stripped-of-patent-rights
Here's one I didn't even know about, if you ever posted a job ad on LinkedIn a company might have contacted you threatening to sue you for violating their patent (Whiskey Tango F....):
https://www.zdnet.com/article/open-source-fights-back-we-wont-get-patent-trolled-again/
A few years ago, CloudFlare did have their own brawl with some as well, and it looks like thankfully they did not backdown:
https://blog.cloudflare.com/three-new-winners-of-project-jengo-and-more-defeats-for-the-patent-troll/
Small mobile app developers were hit for having links to payment providers:
https://www.bbc.com/news/technology-14682700
Apple was sued over Facetime, of all things, and lost:
https://www.bbc.com/news/technology-20236114
I ask here, because this is one of the most pro-startup pro-hacker anti-patent troll communities on the web, does anyone know if there's any org doing something about patent trolling? It's ridiculous that people who provide no value to society can just waste court time and siphon funds from hard working people just because they bought a patent. The patent framework needs to be reworked to force a patent holder to prove they are actually using their patent to build something, or show they have built something that is on the market, and that the violations endanger their efforts, if they cannot produce this, they should be made to pay all court and lawyer fees.
Just my thoughts, I'm sure there's better ways to handle it, but if the rules are changed to stop patent trolls from basically extorting hard working people who actually invent and produce things, I think we could see a lot more. In the giant shift of AI since Mycroft was birthed, I can't imagine how much more advanced Mycroft would have been by now.
I hate when simple things stifle innovation. I'm an innovation junkie, I want to see the future we all saw as kids in cartoons and scifi that fascinated us, but we're often held back by bad actors.
Are there orgs or initiatives? Is anyone ... and I hate the word, but it really should be done, is anyone lobbying to protect inventors from patent trolls? Because they do more harm than good, and they benefit no one but themselves, while again, stifling innovation.


A.I. Writes Buggy Code. A Silicon Valley Startup Wants to Fix It.


Yann LeCun's AI startup raises $1B seed round.


China AI Startup Moonshot Seeks $10B Value in New Funding.


Ask HN: What makes early-stage AI accelerators useful (and what doesn't)?. Hi HN — we recently launched the Berkeley Xcelerator (https://rdi.berkeley.edu/xcelerator), a non-dilutive accelerator program run by Berkeley RDI (https://rdi.berkeley.edu/) for pre-seed and seed-stage teams building in AI and agentic AI. We’d love to get some feedback from the community!
Over the past three years, Berkeley Xcelerator has supported 110+ teams across AI, cybersecurity, and decentralized technologies, whose founders have gone on to raise $650M+ in follow-on funding, spanning 100+ countries.
Some concrete details about the Xcelerator itself:
- The program is non-dilutive (no equity taken)
- Open to pre-seed and seed-stage AI / agentic AI startups
- No UC Berkeley affiliation required
- Selected teams receive support through Berkeley RDI’s research community and ecosystem partners
- Enablement includes cloud, GPU, and API credits from industry partners (including Google Cloud, Google DeepMind, OpenAI, and Nebius, with more to be announced)
- The program culminates in a Demo Day at the Agentic AI Summit (Aug 1–2, 2026) at UC Berkeley, where we are expecting 5,000+ in-person attendees
Here’s what we’d really like input on:
- If you’ve built or joined an early AI startup, what actually helped you most early on?
- If you’ve done an accelerator, what helped and what was a waste of time?
- For technically deep projects (infra, agentic systems, safety-sensitive work), what kinds of feedback or structure mattered most before product-market fit?
If you’d like to apply to the Berkeley Xcelerator, applications are open through the end of February. (https://forms.gle/KjHiLAHstAvfHdBf7)


Show HN: Berkeley Xcelerator – early-stage AI and agentic AI accelerator. Hi HN — we recently launched the Berkeley Xcelerator (https://rdi.berkeley.edu/xcelerator), a non-dilutive accelerator program run by Berkeley RDI (https://rdi.berkeley.edu/) for pre-seed and seed-stage teams building in AI and agentic AI.
Over the past three years, Berkeley Xcelerator has supported 110+ teams across AI, cybersecurity, and decentralized technologies, whose founders have gone on to raise $650M+ in follow-on funding, spanning 100+ countries.
Some concrete details about the Xcelerator itself:
- The program is non-dilutive (no equity taken)
- Open to pre-seed and seed-stage AI / agentic AI startups
- No UC Berkeley affiliation required
- Selected teams receive support through Berkeley RDI’s research community and ecosystem partners
- Enablement includes cloud, GPU, and API credits from industry partners (including Google Cloud, Google DeepMind, OpenAI, and Nebius, with more to be announced)
- The program culminates in a Demo Day at the Agentic AI Summit (Aug 1–2, 2026) at UC Berkeley, where we are expecting 5,000+ in-person attendees
Happy to answer questions here! Your feedback and participation are incredibly valuable to us.
Applications are open through the end of February (https://forms.gle/KjHiLAHstAvfHdBf7)


Ask HN: Best option for hosted agent in 2026?. Hey everyone. I have a simple question and have had a very tough time finding the answer.
Our startup wants to build and use an agent. I want to give the agents skills, access to our APIs, etc. I do not want to reinvent any wheels, I do not want to reinvent an agent loop.
Ideally, we'd effectively have a hosted version of Claude Code with company specific skills, an API access layer, etc. In the way that Claude Code / Cursor has access to our codebase + skills, thats effectively what we want and then start exposing it in a limited fashion to our internal team and then later to our customers. And, for example, add specific features to our existing product such as "summarize this with AI" but instead of that api call going to a model provider, it would go to 'our' agent.
Does this make sense?
Could someone please point me in the right direction on what the best / simplest solution would be? I feel like it should be easy and obvious but I'm struggling to find a clear path forward. Thank you all! - Mike


Harvard’s $699 startup bootcamp offers AI avatars of its instructors. In the HBS Foundry program, AI avatars provide feedback during practice pitches and board meetings.


Show HN: Active Source of Truth for Your Coding Agents. Howdy! Happy Saturday everyone!
As a solo founder, I have always tried to maximize my speed by letting coding agents build as much as possible in parallel. However, as an engineer, I don't trust that AI will always make the right decisions and work with the right context. In the past, I always needed to click through my sessions to glance at the AI's output, try to understand what it was doing, and hopefully steer it or stop it in time.
As a result, the maximum number of concurrent sessions I could manage at once was only 4. I didn't want to be the bottleneck, so I built Meetless Agent (MLA). It basically does what I had to do manually before:
- Monitors the coding agent's tasks and actions to supply it with the correct, up-to-date context.
- Continuously reconciles running information (such as provided/tagged documentation, the agent's output, and the agent's decisions) to actively maintain the source of truth at all times.
- Keeps track of the current rules for the repo, and if an action triggers a registered rule, the rule is injected into the agent context.
My benchmarks show that running coding agents with the help of an active monitor improves quality and accuracy, consumes fewer tokens, and finishes faster: https://research.meetless.ai/stale-context/
Of course, the agent alone can't decide the source of truth; it requires human review and decisions for contradictions, etc. But for the most part, it can safely build a consistent ontology of the current source of truth.
From this, I want to build an AI layer to maintain the source of truth across the business, so I will release more connectors for Slack, Jira, Confluence, etc., soon. The goal is for this AI to assist in every part of the business. Eventually, the same coordination layer will understand that a decision made in Slack affects a Jira task, a document, an email conversation, and what a coding agent should do next without every tool becoming another isolated memory silo.
The coding agent connector is open source at:
https://github.com/Meetless/mla
I am looking forward to your feedback!


Show HN: Gene Inspector Pro – I built a tool for exploring your own genome. Hi HN, I’m Sergey.
When my son was diagnosed with several health conditions, a doctor told us to “learn to live with it.” I refused and started looking for answers.
I spent the next seven years teaching myself genetics and cellular biology. That search led me to build Gene Inspector and have my family’s DNA sequenced. I now study genomic data from 14 family members across three generations.
Gene Inspector turns raw DNA data into a shortlist of findings people can investigate. Instead of searching through millions of variants, users can quickly find potentially interesting ones, explore how they relate to health topics, pathways, or medications, and see the evidence behind each result.
Gene Inspector supports data from common DNA tests, Whole Exome Sequencing, and Whole Genome Sequencing. Findings are annotated using public genetic sources and linked to the underlying evidence.
I also built a pipeline that processes open-access papers, finds claims about specific variants, and links each claim to its source. This lets users check the research themselves instead of trusting a black-box interpretation.
Because most people are not geneticists, I’m now building an AI research agent Diana that explains what users are seeing and helps connect the dots.
Diana will wear two "hats". Her "geneticist" hat will make her strict and skeptical, focusing on stronger clinical evidence. Her "functional medicine" hat will look more broadly at metabolism, enzymes, pathways, and common variants - just like a functional MD would do.
Diana already supports text and real-time voice conversations but is currently available only through an invitation-only beta.
I built most of Gene Inspector's by hand before the AI wave.
I’d appreciate one simple piece of feedback: would you find Gene Inspector useful?


Ask HN: Who needs funding for DB research?. This is the sibling post of “Who wants to fund DB research?“ [1], please see that post for context. In short, if you are working on non-AI related DB topics and are looking for funding, leave a comment below with a short summary of your work. I’m focusing on non-AI areas because their funding has been declining largely due to the spending on AI. Format:
Name, Affiliation
Research topics
[1]: https://news.ycombinator.com/item?id=49336147


Ask HN: Who wants to fund DB research?. You may have heard there’s a funding crisis for science in general, and for computer science in particular. Within CS, despite billions of dollars being spent for AI research, there is less and less funding for traditional areas. I work in Databases, and have seen many colleagues switching to AI-related work like vector DBs and agent systems, even though there have been lots of exciting breakthroughs on core DB topics like WCOJ and Datalog, to name a few. As I write this, a post on DuckDB v2.0 is top 1 of Hacker News, with many users commenting it’s one of their favorite pieces of software. DuckDB is the direct result of decades of DB research [1].<p>HN has a tradition of “who’s hiring” posts, and I figured we could also do a “who’s funding” post. If you are interested in funding DB research, feel free to leave a comment, or reach out to me directly [2]. If you are working in DB and are looking for funding, leave a comment under this post: [3].<p>[1]: https://duckdb.org/why_duckdb#standing-on-the-shoulders-of-giants<p>[2]: https://remy.wang<p>[3]: https://news.ycombinator.com/item?id=49336154


Ask HN: €300/mo to capture work you're doing. Tips how to reach right people?. Hey Everyone,
My name is Salan and I'm a Researchrer at https://pdoom.org/ an early-stage AI Research Lab building LLMs for long-horizon tasks in Europe. We were funded by the German Government (SPRIND).
Currently, we are trying to acquire data, specifically from people who do open-source work/research. Therefore, we pay people €300 per month to capture their digital workflows.
We also built a custom tool that is privacy-preserving specifically for capturing these workflows: Get paid to record your work · p(doom)
Since we have a high quality bar and need really strong people in their fields, finding participants hasn’t been easy. Do you guys have any tips or ideas on how we can successfully reach these profiles?
Any help is highly appreciated!


AI is finding sperm where doctors couldn't.


Ask HN: What tools are you using for human code review of AI-assisted code?. A good proportion of us and our colleagues are now churning out agent-assisted code at an incredible rate, with some of it that is actually good, and a lot that is not so good. I'm personally finding that the real quality gate for our projects is now how thoroughly the generated code was human reviewed to ensure that it is not just correct, but architecturally sensible.
AI code review tools like coderabbit and copilot, or even pointing claude code at a PR are all generally pretty good at finding bugs and style nits, but less good at finding duplicate code, module cross coupling, bad separation of concerns, and so on, even if prompted to do so.
I'm finding that github's PR interface is not really cutting it for me, it was janky even when the reviews were small, but now at the size they're at, it is becoming unmanageable.  Add to that the extra noise of mixing in agent reviews, and people "meat-proxying" in copy-pasted agent output, and it's getting pretty noisy and difficult to navigate.
What have you all found that works well for streamlining human review of AI assisted code? Tools and process suggestions are welcome.


Tax Law Is Funding the AI Infrastructure Boom, Not Creating It.


Different SAST/DAST scanners only share %1.8 of common findings on same source.


Active Inference as Context Acquisition for AI Agents.


Why can AI generate Super Mario but not a wedge ramp for my robot vacuum?. I've been puzzled by something: AI generation can produce an elaborate
  figurine, a cartoon character, even a convincing Super Mario — yet it
  can't reliably make a simple wedge ramp so my robot vacuum can climb a
  step.

  For context: I bought a Bambu P2S but can't model. I tried the "describe
  it and get a model" AIs — the output is unusable, you can't adjust it,
  it's never quite what I meant. I tried having an agent write Python to
  build geometry directly — it tops out at simple primitives.

  What finally worked: geometric decomposition. I break a complex part into
  ordered, grouped steps, describe each as a small spec, and let an agent
  execute them in Blender (via blender-mcp). That process turned out to
  abstract into a small engine — the key insight being it converts the 3D
  spatial reasoning LLMs are bad at, into the structured code they're good
  at. I wrote it up here: https://github.com/zhuchaokn/spec-3d-model

  My questions:
  - Why is "functional part" generation so much weaker than
  "figurine/aesthetic" generation? Is it data (no parametrized-CAD training
  sets), representation (mesh vs B-rep), or evaluation (nobody benchmarks
  "does it print / is it watertight")?
  - Is "turn 3D modeling into code for an LLM" the right framing, or am I
  missing something better?


Show HN: Neolabs.fyi – 100 new AI labs by research area, valuation, and more.