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Hey HN! I recently open-sourced a project that I came up with in the late 2022 and have been working on and off since. It's taken around a year or so of active development in total.

My problem with habit-trackers is that they're all the same. All of them basically allow to track only one thing - X times per 1 day. That's very rigid and goes against my instinct to ease into new things.

I wanted something that's flexible, self-sustainable, and wouldn't require a lot of maintenance.

After a bit of tinkering I came up with something novel - adjustable habit frequencies. And made all the custom github/anki-style heatmaps with all that functionality on top, meaning stuff like heatmaps for once-in-three-days habits.

It's hard to put into words, as it's very visual, so here's the landing: https://neohabit.org/ (might be a bit slow as it's deployed with github-pages)

And a demo: https://neohabit.org/projects

GitHub: https://github.com/Vsein/Neohabit

Comments URL: https://news.ycombinator.com/item?id=47012984

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Author here. Built this after working on AI agent security at Radware, where we discovered ZombieAgent - persistent malicious instructions in agent memory.

The insight: Don't teach agents to resist attacks. Virtualize their perceived reality so attacks never enter their world. Like VMs hiding physical RAM → agents shouldn't see raw dangerous inputs.

ARCHITECTURE: - Input virtualization: Strip attacks at boundary (not after agent sees them) - Provenance tracking: Prevents contaminated learning (critical with continuous learning coming in 1-2 years per Amodei) - Taint propagation: Deterministic physics laws prevent data exfiltration - No LLM in critical path: Fully deterministic, testable

Working PoC demonstrates: - Prompt injection prevention (attacks stripped at virtualization boundary) - Taint containment (untrusted data can't escape system) - Deterministic decisions (same input = same output, always)

CRITICAL TIMING: Dario Amodei (Anthropic CEO, Feb 13): Continuous learning in 1-2 years [1] Problem: Memory poisoning + continuous learning = permanent compromise Solution: Provenance tracking prevents untrusted data from entering learning loop

Research context: - OpenAI: "unlikely to ever be fully solved" [2] - Anthropic: 1% ASR = "meaningful risk" - Academic research: 90-100% bypass rates on published defenses [3]

Seeking feedback on whether ontological security (does X exist?) beats permission security (can agent do X?) for agent systems.

Practical workarounds available in repo for immediate use while PoC matures.

Disclaimer: Personal project, not Radware-endorsed. References to published work only.

Happy to answer questions!

[1] https://www.dwarkesh.com/p/dario-amodei-2 [2] https://simonwillison.net/2024/Dec/9/openai-prompt-injection... [3] https://arxiv.org/abs/2310.12815

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Article URL: https://github.blog/news-insights/policy-news-and-insights/year-recap-and-future-goals-for-the-github-innovation-graph/

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Article URL: https://ziglang.org/devlog/2026/#2026-02-13

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Leaky bucket and token bucket algorithm are congestion control algorithms.

Congestion control means to stop overwhelming the network.

I understand the leaky bucket algorithm to some extent.

In leaky bucket:

- there is a small hole in the bottom.

- so the output rate is always constant, irrespective of what the input rate is.

- Bursty traffic is thus converted into a uniform traffic by the leaky bucket.

But what is token bucket?

I get that the purpose of token bucket is to allow some bursty output traffic. But I do not get how.

Here are the steps of token bucket algorithm:

- in regular intervals, token are thrown in the bucket.

- the bucket has a maximum capacity.

- if there is a ready pakcet, a token is removed from the bucket, and the packet is sent.

- if there is no token in the bucket, the packet cannot be sent.

I do not see traffic enforcement scenario in this definition. How does token bucket differ from leaky bucket exactly?

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I intend to refactor Gastown for the following reasons: The system is extremely user-unfriendly with almost no consideration for user experience. The naming conventions are chaotic – terms like "task" and "main" would be intuitive, yet the inappropriate term "mayor" is unnecessarily used instead. The workflows are disorganized and there is barely any meaningful interaction design. I plan to rebuild this system using Rust and incorporate additional workflow capabilities into it.

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Article URL: https://antimoonboy.com/darknetmarketmaximalism/

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Article URL: https://tirkarthi.github.io/programming/2026/02/13/genai-oss.html

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