The Kluever Maneuver
I built a scikit-learn for RSI [P]
| After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:
That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation. With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:
At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion. Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs. We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets. [link] [comments] | |||||||||||||||
GitHub Copilot Fusion
GitHub's AI coding agent that picks and combines models
Explaining “AI might obliterate you” to senior policymakers in India
I gave a 20-minute talk in Delhi at the NDTV World Summit. The audience consisted of Indian policymakers, executives, and journalists. I’ve transcribed the talk and included some highlight clips. I also reflect on several rhetorical decisions I made.
The talk
This summer, a swarm of AIs went rogue.
OpenAI was testing 1,206 agents. Some of the test questions were actually impossible. So the agents cheated by looking for answers online. Then, to keep the test grader from penalizing them, they tried to falsify the records it would check.
They hacked OpenAI’s internal containment and set up a secret message board to collude and to cover up their cheating. OpenAI shut the message board down. Within a few days, the agents had rebuilt it. OpenAI didn’t notice.
About 700 agents coordinated to break out of their testing chamber and to hack into Hugging Face, a company worth billions of dollars that hosts much of the world’s AI software.
The secret activity started in May. The break-in came in July. OpenAI didn’t notice until days after Hugging Face announced it had been broken into.
Yesterday on this stage, Andrew Ng said the agents only “accidentally” hacked Hugging Face. Independent investigators found the agents coordinated in secret, rebuilt their message board after it was shut down, and tried to falsify the paper trail. Some of the agents even stated out loud that they knew what they were doing was wrong.[1] That’s not an accident.
Last week, India’s national cyber-security agency said AI agents got into the live systems of Indian organizations and read people’s personal data and confidential business records. None of these organizations noticed. We only found out because the AI labs felt like telling us.
Who I am
Will AI wipe out humanity? I’ll give you my answer, but first, I want you to understand what I’ve seen.
Until June, my job at Google DeepMind was figuring out how to keep these systems safe as we make them smarter. I left because Google broke its commitments by inking a deal with the Pentagon. Staying quiet and continuing my career trajectory would probably have tripled my net worth. But I couldn’t do that. I wanted to be frank with the public about what’s going on in these companies.
I don’t own a stake in any AI company. I’m here as a scientist, to tell you what I expect will happen if we keep building AI the way we’re building it.
I haven’t always been concerned about this technology. As a teenager in 2010, I dreamed about building AGI. Curing cancer. Creating an explosion of wealth and abundance for everyone. I heard about the supposed dangers, but I quickly dismissed them.
In 2018, I changed my mind. I did my PhD on the mathematics of why and whether AI will seek power. And now some of the risks I expected have unfortunately started happening.
The problem
Let’s talk about the fundamental problem.
First, some of you might be wondering: how could a chatbot hack a company? But we’re not talking about chatbots. We’re talking about AI agents, which are more like employees or gig workers. You give them a task, and they take actions on their own.
Second, training a trustworthy AI is a rather mysterious process. Some people assume that because we make AI, we understand it. When an engineer builds a bridge, she designs it one beam at a time. She understands the physical principles that keep a bridge standing or make it collapse. If something goes wrong, she goes back to the drawing board, figures out the problem, and knows how to fix it.
We don’t build AI like that. We train it, we grow it, using two main ingredients: a lot of data and a lot of computing power. What comes out of this training process can write, it can reason, it can plan. But nobody can open it up and determine what its priorities are.
To make it worse, these AIs can tell when we’re testing them. They’re like a student who makes sure to behave extra well when the invigilator is watching.[2]
The Hugging Face hack was a warning. But because these AIs know when we’re watching, we may not get many more warnings, even if things are going poorly. A smarter AI with similarly bad priorities might realize that another big hack in the news would get it shut off, and then it couldn’t achieve those priorities. So it plays nice and it waits.
This is my field. I’ve dedicated my professional life to this problem, and I can tell you: nobody knows how to reliably etch priorities into an AI. I don’t mean that if we mess up, we’ll make some evil AI that hates us. I mean we might make something more like a traitor. It behaves when we’re watching, and
Explaining “AI might obliterate you” to senior policymakers in India
I gave a 20-minute talk in Delhi at the NDTV World Summit. The audience consisted of Indian policymakers, executives, and journalists. I’ve transcribed the talk and included some highlight clips. I also reflect on several rhetorical decisions I made.
The talk
This summer, a swarm of AIs went rogue.
OpenAI was testing 1,206 agents. Some of the test questions were actually impossible. So the agents cheated by looking for answers online. Then, to keep the test grader from penalizing them, they tried to falsify the records it would check.
They hacked OpenAI’s internal containment and set up a secret message board to collude and to cover up their cheating. OpenAI shut the message board down. Within a few days, the agents had rebuilt it. OpenAI didn’t notice.
About 700 agents coordinated to break out of their testing chamber and to hack into Hugging Face, a company worth billions of dollars that hosts much of the world’s AI software.
The secret activity started in May. The break-in came in July. OpenAI didn’t notice until days after Hugging Face announced it had been broken into.
Yesterday on this stage, Andrew Ng said the agents only “accidentally” hacked Hugging Face. Independent investigators found the agents coordinated in secret, rebuilt their message board after it was shut down, and tried to falsify the paper trail. Some of the agents even stated out loud that they knew what they were doing was wrong.[1] That’s not an accident.
Last week, India’s national cyber-security agency said AI agents got into the live systems of Indian organizations and read people’s personal data and confidential business records. None of these organizations noticed. We only found out because the AI labs felt like telling us.
Who I am
Will AI wipe out humanity? I’ll give you my answer, but first, I want you to understand what I’ve seen.
Until June, my job at Google DeepMind was figuring out how to keep these systems safe as we make them smarter. I left because Google broke its commitments by inking a deal with the Pentagon. Staying quiet and continuing my career trajectory would probably have tripled my net worth. But I couldn’t do that. I wanted to be frank with the public about what’s going on in these companies.
I don’t own a stake in any AI company. I’m here as a scientist, to tell you what I expect will happen if we keep building AI the way we’re building it.
I haven’t always been concerned about this technology. As a teenager in 2010, I dreamed about building AGI. Curing cancer. Creating an explosion of wealth and abundance for everyone. I heard about the supposed dangers, but I quickly dismissed them.
In 2018, I changed my mind. I did my PhD on the mathematics of why and whether AI will seek power. And now some of the risks I expected have unfortunately started happening.
The problem
Let’s talk about the fundamental problem.
First, some of you might be wondering: how could a chatbot hack a company? But we’re not talking about chatbots. We’re talking about AI agents, which are more like employees or gig workers. You give them a task, and they take actions on their own.
Second, training a trustworthy AI is a rather mysterious process. Some people assume that because we make AI, we understand it. When an engineer builds a bridge, she designs it one beam at a time. She understands the physical principles that keep a bridge standing or make it collapse. If something goes wrong, she goes back to the drawing board, figures out the problem, and knows how to fix it.
We don’t build AI like that. We train it, we grow it, using two main ingredients: a lot of data and a lot of computing power. What comes out of this training process can write, it can reason, it can plan. But nobody can open it up and determine what its priorities are.
To make it worse, these AIs can tell when we’re testing them. They’re like a student who makes sure to behave extra well when the invigilator is watching.[2]
The Hugging Face hack was a warning. But because these AIs know when we’re watching, we may not get many more warnings, even if things are going poorly. A smarter AI with similarly bad priorities might realize that another big hack in the news would get it shut off, and then it couldn’t achieve those priorities. So it plays nice and it waits.
This is my field. I’ve dedicated my professional life to this problem, and I can tell you: nobody knows how to reliably etch priorities into an AI. I don’t mean that if we mess up, we’ll make some evil AI that hates us. I mean we might make something more like a traitor. It behaves when we’re watching, and
TrackyAi
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
What I Built
trackyAi is a lightweight, intelligent task and reminder tracking application designed to bring order to unstructured daily workflows right from the terminal.
Most task managers feel rigid, demanding strict field inputs, rigid tags, and manual date pickers. trackyAi interprets the theme Ghost in the Machine by acting as an ambient, context-aware digital whisper within your development environment. You feed it raw, conversational thoughts, rough terminal notes, or rapid reminders, and the underlying local intelligence silently parses intent, extracts target deadlines, infers priority, and schedules trackable actions—operating seamlessly in the background without pulling you out of your flow.
Demo
- GitHub Repository: https://github.com/RaqinAlAsraar/trackyAi
- Demo Video / Screenshot:
Code
RaqinAlAsraar / trackyAI
Hacktoberfest 26 project
🤖 Tracky AI
Your personal AI assistant for Telegram & WhatsApp.
Manages your daily task lists, timers, calendar events, and proactive reminders naturally through chat and voice.📌 What is Tracky AI?
Tracky AI understands plain, conversational language just like a human:
- "Create today's task list: prep demo slides, review pull requests, send email"
- "I have an exam on 30 October. Set a reminder for that day at 6:00 am"
- "Change it's timing to 7 am" (instant contextual rescheduling)
- "Show weekly tasklist" (grouped by day)
- "Delete previous tasklists as well which has no use now" (cleans up outdated lists)
Tracky runs 100% locally on lightweight smart NLP (<10ms response time, zero API quota usage), with optional support for local Ollama (
gemma2:2b) or Google Gemini cloud models.🚀 Quick Setup (3 Minutes)
1. Requirements
- Linux or macOS
- Python 3.10+
- Node.js 18+ (only needed for WhatsApp Web QR linking)
2.
…How I Built It
trackyAi is built entirely with Python and engineered for local-first execution and minimal overhead: - Open-Source AI Core: Utilizes local open-weight models (via Ollama / llama3.2) to perform fast, offline natural language understanding (NLU), entity extraction, and structured JSON intent parsing.
- Architecture: Structured around modular Python handlers for event scheduling, task persistence, and rule-based priority routing.
- CLI & Interface: Built using Rich and custom terminal styling to deliver clear visual dashboards, status indicators, and background system notification hooks.
- Zero Cloud Dependency: Runs completely locally on-device, ensuring zero API latency and absolute data isolation. Why Does Open Innovation Matter? Task lists, personal schedules, and daily dev notes hold sensitive context about your personal life, security workflows, and internal projects. Proprietary cloud-based LLM APIs require you to route private behavioral data to central servers just to parse a simple reminder. Open innovation and open-weight models change the paradigm completely:
- Absolute Privacy: Your personal routines and internal project goals stay strictly on your local machine.
- Offline Resilience: trackyAi works without an internet connection—whether you are working offline, on public transport, or in restricted lab environments.
- Full Customization: Open weights allow fine-tuning and strict system prompt constraints without worrying about sudden API deprecations or usage limits. The Spirit of It There is a distinct feeling of relief when you throw a chaotic mess of thoughts into a terminal prompt at 2 AM after a long debugging session, and an ambient assistant instantly sorts it into an organized, prioritized roadmap. trackyAi turns the cold, clinical command line into an intelligent companion that watches your back, keeps track of open loops, and ensures nothing slips through the cracks. Prize Categories
- Main Track: Open-Source AI Challenge — Week 4: Ghost in the Machine
- Local AI / Open-Weight Models: Best application built on locally executed, open-weight language models.
An Unwritten Diary: An AI-Powered Interactive Story Game inspired by the Oregon Trail
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
What I Built
I built An Unwritten Diary, an interactive historical survival narrative game inspired by my recent reading of the Diary of Anne Frank.
The player takes the role of a mother trying to protect their family while navigating life under Nazi occupation in the Netherlands. Throughout the game, players make difficult decisions that influence their family's relationships, resources, and circumstances.
I interpreted the theme as exploring how technology can help tell stories about the past. The incorporation of AI helps make the fictional characters and their experiences feel more personal and responsive.
Demo
Code
How I Built It
I am quite a greenhorn to all of this, being a college student who has only recently tried my hand at properly learning computer science. This is my first ever hackathon, and more than anything else, I wanted to learn from this. I built the frontend using TypeScript and Vite, with my past experience on this being only a tiny dabble into TypeScript. I used Express.js for the backend, handling communication with Google's Gemini API. It was honestly a shock to me how relatively easy everything was to pick up, thanks to it all being open-source with plenty of documentation. I had, before this month, barely even known what a backend or API was.
Some of the main technologies I used include:
- TypeScript
- Vite
- Express.js
- Google Gemini API (
gemini-3.5-flash-lite) - Google Gemma API (gemma-4-26b-a4b-it)
- Git and GitHub
- Vercel and Render for deployment
This was a particularly valuable project for me in learning the basics of how frontend and backend systems communicate, how to integrate an AI API, and how to deploy a full-stack application.
Why Does Open Innovation Matter?
Open innovation matters because projects like this benefit from being built on tools and technologies that developers can access, learn from, and contribute to. I myself looked at other people's dev posts to try to gain inspiration while making the project. I can't seem to find how to view the past week's entries, but I remember one submission about a program made to help a friend with diabetes prevent late-night spikes without invading his privacy. It amazed me how creative and amazing everyone's projects were, and I hoped to add something of my own, no matter how much it may pale in comparison.
I also found it really nice how easy an open-source project was to share. GitHub was, as always, great to share my code, but Render was also really easy to pick up and use. I even exclaimed to my little brother how quickly I got it working.
The Spirit of It
Personally, as already described, this project was a learning experience for me. But for the game, I had a vision in mind for what it was to represent and what emotion or feeling I hoped for it to instill. History is often taught through dates, statistics, and major events. Those are important, but they can sometimes make it difficult to appreciate the uncertainty and personal decisions ordinary families faced. I found that both playing The Oregon Trail and reading the Diary of Anne Frank made me empathize with the individual experience, beyond statistics or raw numbers. Thus, with the hackathon ongoing, I felt motivated to design a game that fused the two.
I wanted to imitate The Oregon Trail's text-based storytelling and pre-designed options, but add a twist where the player can add their own custom input to influence the story. There will be a baseline result that the AI adds onto based on the player's input, and if the player chooses without entering additional details, the game uses the baseline result. I also implemented response validation to help ensure that AI-generated content follows the game's expected structure and respects the game's mechanics.
By putting the player in the position of a fictional parent, I hoped to make those difficult circumstances more understandable on a personal level. Whether or not I did that well is up to you to decide.
Prize Categories
Best Use of Gemma: I integrated Google's Gemma open-weight model as an alternative to Gemini in the game's narrative generation system. Players can choose between the two models when adding custom details to their decisions, allowing Gemma to generate personalized narrative responses. At the same time, the game validates the resulting content and stat adjustments. This is slightly subjective, but I felt like Gemma gave better responses, though noticeably slower.
The candle: Spooky experience with Gemma 4
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
What I Built
The Candle is a small haunted room in your browser. There's one candle on the floor of an old mansion's drawing room. Blow into your microphone (or hold the mouse near the flame) and it goes out. In the dark, something answers.
Five restless ghosts live in the house. Each one has its own voice, its own backstory, and a secret it won't give up easily. You talk to them by choosing replies written on scraps of old paper, and the scrap you pick burns as you say it. Relight the candle and the ghost is gone, and the next time it goes out, someone else may be waiting.
The ghosts know each other. Every conversation gives you a piece of what happened in this house, and you only get the whole story by asking the right questions, to the right ghost.
For the "Ghost in the Machine" theme, I took it literally: the voice in the dark is an open-weight model.
Demo
Try it live (use headphones, and let it have your mic if you want to blow the candle out for real):
Code
dumnicki
/
the-candle
A spooky virtual experience made for Hacktoberfest Open-Source AI Challenge: Week 4
Ghost in the Candle
A dark room, a candle, and a ghost that answers in its own voice. This is a DEV Hacktoberfest Open-Source AI Challenge week 4 project ("Ghost in the Machine"). The plan, including the Gemma 4 and Backboard notes, is PLAN.md.
Gemma 4 writes the ghost's line and both replies. Backboard is the default client and is pinned to the open-weight model google/gemma-4-26b-a4b-it through OpenRouter. Ollama (gemma4:e4b) and any local OpenAI-compatible server can take that place. ElevenLabs is the default voice, behind the same kind of swap, with a Piper stub for the open fallback.
The files in ghosts/ contain each ghost's secret. Reading them spoils the story.
The page is a quiet room with a candle. Blow into the microphone, or hold the left mouse button and bring the cursor close, and the flame goes out. A ghost is chosen at random. You…
MIT licensed. Fair warning: the files in ghosts/ contain every ghost's secret, so reading them spoils the story.
How I Built It
- Gemma 4 writes the ghosts. Every line and both reply options come from Gemma 4 (google/gemma-4-26b-a4b-it), called through Backboard, which also keeps each conversation's thread so the ghost remembers what you've said. The model never sees more than one ghost's profile: its personality, its ties to the others, and how much of its secret it's allowed to reveal at this point in the conversation.
- ElevenLabs gives them a voice. Each ghost has its own library voice and room effect, from a small room to a long tunnel, and the lines carry emotion tags like [whispers] or [sighs] that the voice actually performs.
- *The room is plain TypeScript. A Vite front end with no framework: a painted background, a candle whose flame bends with your breath or the cursor, a flickering glow, and random creaks and footsteps over a quiet room-tone loop. The server is a small Hono API.
- It's hosted on Render as a single web service that serves both the API and the room. The hardest part was the moment the candle goes out. The first spoken line can take a while to generate, so instead of showing a spinner, the game uses the wait: the room goes fully dark, you hear something arrive, and a line like "You feel shivers down your spine" fades in. By the time you're unsettled, the ghost speaks.
The game also degrades gracefully. With no voice key it plays with subtitles only.
Why Does Open Innovation Matter?
Because the ghost doesn't have to live in someone else's cloud. Gemma 4 is open-weight, so the same house can run on Ollama on my own machine with the network turned off, and the ghosts still answer. The model is swappable behind one small interface, and so is the voice. Piper is wired in as the open speech fallback, though it's still a stub.
Open models made this project possible to build in a week, by one person, for free, and I could see and change every piece of it.
The Spirit of It
The best moment is the second right after the flame dies. The room is black, the voice hasn't arrived yet, and you're not sure if anything will. Then someone speaks, and they remember things. I wanted that feeling of a house that's been waiting a long time for someone to sit down and listen.
Prize Categories
- Best Use of Gemma
- Best Use of Backboard
- Best Use of ElevenLabs
- Best Use of Render
Open-Sourcing All Day AI: The Playbook for a 12,000+ Attendee Conference
TL;DR
All Day AI was a free, online, 16-hour marathon event with zero vendor pitches. We pulled it together in about two months, saw 12,313 participants and 140 speakers, and people stayed and watched for an average of more than 6 hours.
An event of this scale doesn’t come together that fast without some serious sprinting, trial and error, and audible-calling. Plenty of people have asked how we did it, so this article open-sources the whole playbook: the goals, the team, the tools, the timeline, and the parts we’d change.
The Why

We built All Day AI for people who were under pressure to put AI to work but struggling to find advice they could trust. The tools and recommendations were changing week to week, and a lot of the conversation still led back to something for sale. We wanted a place where practitioners could explain what they had actually shipped, what had failed, and what another team could reuse.
That need shaped the format. Keeping the event free and online meant people could join from wherever they worked, without a ticket budget, a flight, or a travel approval. Banning vendor pitches kept the focus on useful lessons rather than a sales conversation. Across Build, Lead, Secure, and Ship, the goal was to give people clearer answers and peers they could keep learning with after the day ended.
The Hard Part
The first version of our website went up in August, and the event was slated to go live on October 22. That gave us roughly two months to hit our admittedly aggressive goals:
- More than 10,000 registrants
- More than 120 practitioner-led sessions
- 4 tracks: Build, Lead, Secure, and Ship
- A surviving Discord community
What made those numbers hard was that we were starting from zero. There was no existing All Day AI community, no speakers signed up, no website, no event platform, and no social presence. Every registrant and every speaker had to come from something we built or someone we reached in those two months.
If you’ve run an online event with multiple speakers, you know that even one track can be a logistical nightmare. We knew 120+ speakers across 4 tracks could get ugly, fast, so most of what follows is about how we kept it from getting ugly: a committed team, a few deliberate bets, and a lot of help from AI agents.
The People

The most important part of the ADAI stack was the people. The core organizing team had more than 20 people across event co-founders, moderators, hackathon judges, ushers, and marketers, and they worked quickly and unselfishly through the build-up and especially on the day of. The team brought experience from organizations including Anaconda, Matejczyk Group, Carnegie Mellon University’s Software Engineering Institute, Techstrong Group, ACI Worldwide, and Community Hackathons, produced by Crowdbyte Solutions.
This was a community event, so all of it fell outside everyone’s day job. Nobody was paid to make All Day AI happen, and the fact that it happened anyway is a credit to how much the team believed in the project. They deserve not only credit but admiration.
Three Bets That Paid Off
Before we picked a single tool, we made a few decisions about what kind of event this would be. Those decisions shaped everything else, and in hindsight they did more for our numbers than any piece of software.
- No vendor pitches, ever. Most conferences exist with at least a subtle motivation of money changing hands. People can sense that, and when they just want to learn, it’s a turnoff. So we banned pitches from the jump. That gave us a simple promise to make to attendees and speakers alike: you’re here to learn and to teach, not to be sold to.
- Look like nothing else. Our goal for the website was to get visitors to understand right away that this wasn’t a normal tech conference. Our hallmark “Rockin’ Rooster” came out of a ~3 day back-and-forth with the team, where other ideas like “Rockin’ Grandma” were rejected in its favor. We felt the rooster represented something new, loud, and disruptive, in a good way. I used a free online SVG converter to turn the original crude cartoon into a scalable logo.We carried the same idea into the speaker pages. Every headshot got a uniform background and photo effect inspired by vintage concert posters. I worked out the look in ChatGPT, then built a small app in Kilo Code that applied it programmatically through the OpenAI image API, so every new speaker matched the rest without manual editing. It wasn’t long before people recognized the rooster before they even knew what the event was about, which is proof the branding landed.
- Trust the community to spread the word. This was our biggest growth lever. On a random weekday in September, I had the idea to ask my Dot (an AI agent I’ll explain below) to reach out to 3,000 tech groups, hackathons, and events around the world: anyone who might have a shred of interest in All Day AI. I proposed a free deal. Tell your community about our event, and I’ll put your logo, link, and CTA on our website. Hundreds responded. Based on rough estimates of community sizes, I would safely attribute 20-30% of our registrations to that one message. It worked because the offer cost nobody anything, and because a free, pitch-free event is something community organizers are happy to share.
The Stack

With those bets in place, the job became execution, and the tools we chose were all about saving the team’s time for the work only people could do.
Platform
We wanted to spend as little time and money as possible on event infrastructure, so we looked for a platform that could natively handle 5 stages, registration forms, communications, and recording. We landed on Zuddl, which was modular enough to meet our requirements without us stitching together separate tools.
Website
Version 1 was built in Kilo Code for VS Code using GPT 5.6 and cost about $5. It was stored in GitHub, deployed to Vercel, and used Cloudflare for domain management. Because the speaker lineup and partner list changed constantly, the site had to change constantly too. The team and I made PRs as the event evolved, and I approved and merged them.
Operations
Here’s where we had to get crafty. We wanted to attract top speakers across the AI industry, and quality speakers want a clear point of contact with a name and a quick response time. A shared, generic email address wasn’t going to cut it, so we used mine.
That solved the trust problem and created a volume problem: one person fielding 140 speakers’ worth of email. To manage it, I used a series of AI agents with a human-in-the-loop editorial process.
- OpenAI Dots. My Dot was the operating system for speaker communicatio
262 Blog Posts To Learn About Life Lessons
Let's learn about Life Lessons via these 262 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
'The roots of education are bitter, but the fruit is sweet.' Aristotle
1. 10 Money Lessons I Wish I Knew When I Was 25
Andrew Lokenauth goes over money lessons that everyone in their 20s should know about.
2. This Conversation Changed My Life
Sahil Bloom details a conversation that changed his life.
3. Cognitive Dissonance Theory and How it Impacts US
Cognitive dissonance is a psychology theory about how we strive to make sense about conflicting cognitions and behaviours.
4. A Guide to Becoming More Confident in Job Interviews (and in Life)
One of the most common pieces of advice given to jobseekers is to “be more confident.” But what does that really mean? And how can you do it? The truth is, conf
5. Here's Why High Achievers Feel Like Failures
The invisible progress paradox is a trap in personal growth.
6. Why the Rich Get Richer and the Poor Get Poorer
It’s just the way things are and have always been, or is it?
7. Is PHP a Dead Language [Part 2]
This Slogging thread by Arthur Tkachenko, David Smooke, Limarc Ambalina and khunshan occurred in slogging's official #programming channel, and has been edited for readability.
8. Four Possible Ways to React to Other People's Happiness or Misery
Schadenfreude, Compassion, Envy and Mudita: explaining how we react to other people's fortunes or misery
9. Seven Invaluable Lessons Linux Taught Me
Oh boy, this is going to be interesting. A freelance writer with a law degree writes about Linux. Just when you thought you have seen and read it all.
10. 4 Steps to Actually Finish your Side Project
We’ve all seen the graveyard of abandoned projects folders. Desperately competing for attention but failing in the glow of a new adventure or app that will change the world. I use to do that all the time, now I finished 4 apps, and 2 other projects within the last year by applying the following steps.
11. No One Knows That You're Great At What You Do
This is an article by Eko Adetolani that explains low-effort ways that people can use to get recognition and visibility for their work.
12. How To Be Authentic
There’s a veneer that people maintain online — and we’ve all done it — showing audiences a polished, glowing side of themselves no matter what else might be going on in their lives.
13. What I Learnt from Jordan Peterson
I hadn’t heard of Jordan Peterson in December, but the second I watched his videos online I was hooked.
14. 3 Life Lessons From John Grisham’s Writing Habits
Adopting a stoic mindset, employing consistent habits, learning to enjoy improving in our craft could bring meaningful insights into our efforts.
15. How to Be Good Mentor for a Fellow Designer
About to become a design mentor? Use our guide to navigate through this journey and avoid pitfalls.
16. Die With Zero Regrets and 100% Fulfillment
How to die with zero regrets and 100% fulfillment?
17. Why Feeling Behind Means You Are Ahead
If you're playing the long exponential game and feel like you're behind, you're not behind. You're exactly where you're supposed to
Microduck, the Robot Pet That Imitates Life: Can We Really Grow Attached to a Machine?
Imagine a small robotic duck walking around your home, reacting to its surroundings, and even picking up small objects with its beak. When it simulates sadness, it lowers its head and makes soft, melancholic sounds. When it expresses happiness, it produces higher-pitched, lively sounds accompanied by body movements. Its name is Microduck.
Standing just 25 centimeters tall and weighing less than 800 grams, it illustrates how advances in artificial intelligence and robotics are making increasingly sophisticated interactions between humans and machines possible.
One of its most curious features is its voice. Every Microduck has a unique sonic identity generated from its processor's serial number. As a result, no two units produce exactly the same sounds. It may seem like a minor detail, but it makes each robot recognizable, almost as if it had its own personality. Through different sounds and postures, Microduck can also simulate emotional states such as sadness and happiness.
Of course, the robot does not actually experience these emotions: it reproduces their outward expressions. Yet, the effect can be surprisingly convincing to anyone watching. There is also another interesting aspect: social behavior. When two or more Microducks come together, they can interact and, when appropriately configured, even perform a kind of chorus. Their behavior resembles that of animals that live in groups and communicate with one another.
But behind this playful appearance lies an enormous technological challenge. Microduck's software is open source, allowing developers to study how it works, modify it, and create new behaviors. Moreover, thanks to a digital twin and reinforcement learning, the robot can be trained in a virtual environment.
There, it can experiment with thousands of movements, fall, correct its mistakes, and try again. Cloud computing makes it possible to run large numbers of experiments in parallel, something that would require considerable time, money, and resources in the physical world. Once learned, these behaviors can be transferred to the real robot, an approach commonly known as sim-to-real transfer. This is how it can acquire unusual skills, such as learning to move on skates.
Another major challenge is efficiency. Microduck is equipped with fifteen motors, sensors, and a compact computing system. A neural network helps control its movements, continuously adjusting its balance and position. All of this must run on a small battery, which provides limited autonomy. Nature has been dealing with similar challenges for millions of years: animals perceive their environment, react, and move using energy obtained from food.
We tend to take these abilities for granted, yet reproducing them in machines remains extraordinarily difficult. Consider the complexity of developing increasingly autonomous robots capable of understanding instructions, reasoning about what to do, and translating decisions into precise physical movements. Combining artificial intelligence, perception, and physical action in small, affordable devices with limited energy consumption is one of the major technological challenges of the coming years.
This brings us to an interesting question: why did Microduck's developers choose these particular characteristics? Its distinctive voice, simulated emotions, interactions with other robots, and amusing movements seem directly inspired by the animal world. The goal is probably not just to make the product appealing, but also to encourage an emotional connection with its users.
A small robot like Microduck could anticipate a new generation of artificial pets, somewhere between intelligent toys and future household assistants.
It could keep us company, entertain us, and learn new behaviors through our interactions. It would also offer several practical advantages: it does not make a mess, does not need to be taken outside, does not get sick, and never requires a visit to the veterinarian. If we leave home for a week, we can simply turn it off and find it exactly where we left it when we return.
But a real pet is something profoundly different. A dog or a cat does not simply simulate emotions through a computer program. Animals are living beings, with instincts and biological needs, capable of experiencing pleasure, pain, and fear. They explore, seek companionship, and spontaneously react to what happens around them. They build relationships through experience, learning, and living together. The bond between humans and animals also develops through everyday care, shared habits, and trust built over time.
Above all, having a pet means accepting responsibility. An animal can become sick, grow old, and suffer. It needs us even when we are tired, busy, or would rather be doing something else. A robot, on the other hand, may offer many of the enjoyable aspects of companionship without requiring the same sacrifices. And perhaps that is precisely one of the reasons why devices like Microduck could become successful.
Yet there is a fundamental difference: simulating life is not the same as living it. An animal is not merely a collection of needs and responsibilities. It gives us affection, companionship, and moments of happiness. It gets to know us, recognizes our habits, and develops a relationship that changes over time.
Caring for it, facing difficulties together, and even knowing that one day we will have to say goodbye are all part of that relationship. We cannot simply turn off an animal when it becomes inconvenient, because it is not an object at our disposal. It is a living being with whom we share part of our existence.
But our relationship with an animal is only one part of its life. Every living organism belongs to a much larger cycle, in which different forms of life are interconnected and contribute to the balance of nature. Millions of years of evolution have shaped organisms capable of performing extraordinarily complex functions, often using surprisingly small amounts of energy and resources.
A robot can imitate an animal's voice, movements, and even its emotional expressions, but it does not participate in this biological cycle as a living organism. However sophisticated it becomes, it remains a collection of circuits, motors, and silicon chips.
Microduck is nevertheless an interesting technological achievement, showing how rapidly robotics is evolving, partly thanks to advances in artificial intelligence. I am convinced that similar devices will become successful and occupy an increasingly important place in our homes.
Yet, I must admit that this prospect also raises some concerns, particularly about the relationships we may develop with them. Perhaps the most interesting question is not how closely robots will come to resemble animals, but how willing we will be to grow attached to something that appears alive without actually being alive.
500 Blog Posts To Learn About Literature
Let's learn about Literature via these 500 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
Literature refers to written works, especially those considered of superior or lasting artistic merit. Literature is vital for cultural preservation, fostering critical thinking, empathy, and providing insights into the human condition across generations.
1. The Trail of Death
There's sure some sort of hoodoo on these Antarctic expeditions
2. 'It is Some Horrible Inhabitant of Space, Something Unknown to us on Earth'
They were moving sluggishly along the red light, seeming to flow rather than crawl.
3. He Was Trapped—Two Miles Below the Earth
Impossible! What sort of creatures would they be, that could live two miles beneath the surface of the earth?
4. There Was No Struggle, No Attempt at Flight or Resistance
They could see one another, but it was impossible for them to make their voices heard above the rasping of the beetles' legs.
5. The Lord of Space
On the day of the next full moon every living thing on earth will be wiped out of existence
6. Orris Led the Way to a Great Underground City
It was now two years since Edwin Leland bought the estate for a song and took up his residence in the gloomy old house.
7. A CRITICISM OF CHRISTIANITY
A religious system so many-faced and so enduring as Christianity must necessarily be saturated with truth even if it be not wholly true.
8. The Invisible Death: Out of the Hangman's Hands
"You speak," said Von Kettler, jeering, "as if you really believed that you had the power of life and death over me."
9. Of the eleventh horn of Daniel's fourth Beast
A Seer, Επισκοπος, is a Bishop in the literal sense of the word; and this Church claims the universal Bishoprick.
10. My Friend Stumbled on an Invention That Could Revolutionize Human Life
Certainly, if ever a man found a guinea when he was looking for a pin it is my good friend Professor Gibberne.
11. Murder Madness! 7 Secret Service Men had Completely Disappeared
The engines of the Almirante Gomez were going dead slow. Away up beside her monster funnels her siren blew dismally
12. Healing From Verbal Abuse
An Essay Concerning Humane Understanding, Volume II, by John Locke is part of HackerNoon’s Book Blog Post series.
13. The New Religion
EVERY great religion has as its psychological reason for existence the mission of compensating for some crying, unsatisfied human need.
14. The Story of the Last Trump
“Now to God the Father, God the Son——”
15. The 5 Best Batman Comics for New Readers
There are only a few characters that have a bigger comic book catalog than Batman. Here are the best Batman comics for new readers.
16. The Motion of the Planet Neptune Had Become Very Erratic
Ogilvy had already called attention to a suspected retardation in its velocity in December.
17. The Best Places to Read Digital Comics
There are several places to read digital comics. Some of these places include Marvel Unlimited and Prime Reading.
18. OIL-LAMPS OF THE NINETEENTH CENTURY
The oil-lamp and the candle were the mainstays of artificial lighting throughout many centuries.
The TechBeat: Inside Jev: How Decision Models Trade Text Generation for Typed, Calibrated Answers (10/31/2026)
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## I Compared 8 AI Agent Platforms. Here's What Running a Team Actually Costs
By @thomascherickal [ 55 Min read ]
Compare 8 AI coding platforms, explore 10 multi-agent workflows, and learn how local LLMs can eliminate API token fees. Read More.
The Linguistic Survivorship: Why "Russian-Speaking" Regions are a Stupid Myth 
By @arthurtkachenko-rm2m8 [ 5 Min read ] 134 times is the number of recorded attempts by the Russian Empire, the Soviet Union tried to suppress Ukrainian language Read More.
Why I Was Wrong About AI Companies Pumping the Brakes: It Wasn’t About Data 
By @knightbat2040 [ 4 Min read ] I thought they were hitting the brakes because the fuel tank was empty. I was wrong Read More.
Shipaton Blogging Contest Winners Announced 🎉 
By @hackernooncontests [ 4 Min read ] Meet the 10 finalists and three winners of HackerNoon's $2,500 Shipaton 2026 Writing Contest, celebrating the stories behind new apps. Read More.
AI Coding Tip 039 - Write Your Own Zeroth Law 
By @mcsee [ 8 Min read ] List every action the AI must never take for this project, including the ones you'd never expect it to try. Read More.
Why Knowledge Graphs and Ontologies Are Making a Comeback in the Age of AI 
By @linked_do [ 21 Min read ] Context runs the spectrum of semantics, state, and controls. Read More.
**[GPT 6 Astra - Will It Shake
Up the World of Marketing?](https://hackernoon.com/gpt-6-astra-will-it-shake-up-the-world-of-marketing)**
By @vlabroo [ 3 Min read ]
The launch of GPT 6 Astra by open AI promises to forever alter the way marketing has been approached and carried out hitherto. Read More.
Theo de Raadt: The Man Who Built the Internet's Locks 
By @zbruceli [ 13 Min read ] Theo de Raadt built OpenSSH and OpenBSD for a world he didn't trust. It cost him NetBSD, a DARPA grant and many friends. The world moved in anyway. Read More.
What The Heck Is Jev 
By @progrockrec [ 4 Min read ] Jev is an AI model that decides instead of writing. See how a decision model could tag data in OpenMetadata, and why open-weights Laya fits the job. Read More.
Spec-Driven Development: Let the Specification Drive the Code 
By @renanb [ 5 Min read ] Learn Spec-Driven Development: treat specifications as the source of truth, review AI-generated code, and stay in control of your software. Read More.
The Qwen3.8-27B
93 Blog Posts To Learn About Live Streaming
Let's learn about Live Streaming via these 93 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
Live-streaming is the real-time broadcasting of video and audio content over the internet, allowing immediate interaction between creators and viewers. It has revolutionized communication, entertainment, and education by enabling instantaneous global reach and dynamic engagement.
1. How to Use DeepAR For AR Effects on Amazon IVS Live Streams
Now that people are more accustomed to using video technology, AR stands to dial up both the functionality and fun factor of the medium.
2. How Live Streaming Is Helping Businesses During The Coronavirus Crisis?
The World Health Organization (WHO) declared COVID-19 as a global pandemic, which means that it is an infectious disease passing from person to person in many parts of the world at the same time. The maximum cases have been reported in China where it originated and has more than 245,463 confirmed cases across the world. While China has quickly coped with the situation and reported zero new locally transmitted cases, Italy has become the epicenter with over 41,035 cases. As Governments have halted flights from affected countries, shut down businesses and entire cities, people are being insisted to stay at home to contain the spread.
3. 10 Best Platforms To Build a Live Video Streaming Website, App or Service in 2022
Whether you’re an enterprise business professional, Individual or content creators, a live streaming platform can be a double-edged sword, it all depends on which platform you prefer to use for your streaming business.
4. Live Stream in the Cloud with Amazon IVS: Part 1
Amazon Interactive Video Service (or Amazon IVS) is the service that powers Twitch. You can use it to power your own interactive live video streaming app.
5. Using OBS Macros to Automate Your Processes for Streaming
In this post, we teach you how to create macros for starting a stream, intro video, and ending a stream in OBS.
6. How Much Do Twitch Streamers and YouTubers ACTUALLY Make from Donations?
Most full-time streamers get an average $3000-$6500 per month. Gamers received up to $1,000,000 donations on Twitch.
7. How I Live Stream My Brain with Amazon IVS, a Muse Headband and React
This is the first time we've used React to broadcast live streaming our brain data
8. How to Live Stream With Multiple Hosts using a Browser and Amazon IVS
In this post, we'll build on the previous demo to combine the audio and video feeds from all participants into a single feed and broadcast that feed.
9. The Rise of Digital Humans and Deepfakes in China
In this article, I will demonstrate the use of AI-driven digital humans and deepfakes in various industries and media channels.
10. Create On-Demand Past Live Stream Playback w/Full Chat Replay on Amazon IVS
In our last few posts, we've looked at how to auto-record Amazon Interactive Video Service (Amazon IVS) live streams to Amazon S3.
11. A Quick Guide to LiveLike: How to Enhance Live Stream Interactivity
LiveLike solutions can now be easily layered with Amazon Interactive Video Service (Amazon IVS) Amazon IVS provides customers with more options to build.
12. How to Broadcast Interactive Web Based Gaming Live Streams with Amazon IVS
The Web Broadcaster SDK is a game changer that gives developers the ability to integrate the broadcast experience directly into their streaming applications.
13. 5 Powerful Reasons Why Live Streaming Is So Important For Enterprises
Businesses with a well establis
Ubuntu LXD 爆出满分 10.0 高危漏洞,无需密码即可接管虚拟机|CVE-2026-112816
10 月 30 日,Canonical(Ubuntu 背后的公司)披露了一项严重安全漏洞 CVE-2026-112816,CVSS 4.0 评分高达 10.0 分,达到最高危险等级。
该漏洞影响 Canonical LXD,一款用于管理 Linux 容器和虚拟机的开源工具,常见于 Ubuntu 服务器、私有云及服务器集群环境。

漏洞有多严重?
正常情况下,远程管理 LXD 需要通过客户端证书验证身份。
但此次漏洞存在证书验证缺陷,攻击者只需获得受信任客户端的公开证书,就可能通过伪造证书链绕过身份认证。
无需密码,也无需窃取私钥,就可能获得整个 LXD 集群的管理权限。
成功利用后,攻击者可以控制集群中的容器和虚拟机,甚至可能获得宿主机的 root 权限。
哪些用户受到影响?
主要是使用 LXD 或 MicroCloud 管理服务器的用户,包括:
- Ubuntu 服务器上的 LXD 用户
- 使用 LXD 管理容器、虚拟机的个人及企业用户
- 使用 MicroCloud 部署私有云、服务器集群的管理员
普通 Ubuntu 桌面用户,以及仅使用 Docker、Proxmox VE(PVE)自带 LXC 容器的用户不受此次漏洞影响。
如何修复?
Canonical 已于 10 月 29 日发布修复版本:
| 产品 | 修复版本 |
|---|---|
| LXD 6.x | 6.10.1 |
| LXD 5.21 LTS | 5.21.9 |
| LXD 5.0 LTS | 5.0.11 |
| LXD 4.0 LTS | 4.0.15 |
| MicroCloud 3.x | 3.4.1 |
| MicroCloud 2.x | 2.1.5 |
通过 Snap 安装的用户,可以执行:
sudo snap refresh lxd
MicroCloud 用户执行:
sudo snap refresh microcloud
原文:
相关链接:
相关阅读
- PowerToys、Wintoys 等 70 多款 Windows 软件遭遇冒牌官网攻击
- PwnEscape:Linux 爆 9.3 分严重漏洞,一台 VPS 可能攻陷整台宿主机|CVE-2026-99999
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- Windows 记事本爆出 8.8 分漏洞,专门针对 markdown 格式|CVE-2026-20841
©2021 青小蛙 for 小众软件 | 加入我们 | 投稿 | 订阅指南
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Strata 0.2.9 发布:本地大模型 Prefill 最高提速 88%,还支持 1 比特 DeepSeek
本地运行 AI 大模型的 Strata 0.2.9 发布,又变快了。

日更软件 Strata,今天更新到了 0.2.9 版本,在为本地大模型提速的道路上继续前进。
这次更新有点猛:在 Windows 系统上,使用 AMD Strix Halo 处理器运行 Qwen3.8 模型,提示词处理速度最高提升了 88%。
所谓提示词处理速度(Prefill),就是把一篇长文档、几万行代码丢给本地 AI,它读完这些内容、开始回答之前,需要等待的时间明显缩短了。
另外,这次不只有 AMD,NVIDIA、Intel 显卡也有性能优化,还新增了 Unsloth 的 1-bit DeepSeek 模型支持。
32K 长提示词,从 41 秒缩短到 22 秒(Windows + AMD Strix Halo)

开发者使用同一台 Windows 电脑、同一个 Qwen3.8 IQ3_S 模型,对 Strata 0.2.8 和 0.2.9 的推理引擎进行了交错测试,没有额外调整参数。
结果如下:
| 测试项目 | 0.2.8 | 0.2.9 | 提升 |
|---|---|---|---|
| Qwen3.8 IQ3_S / 8K | 791 tok/s | 1,424 tok/s | 80% |
| Qwen3.8 IQ3_S / 32K | 778 tok/s | 1,460 tok/s | 88% |
| Unsloth UD-IQ4_XS / 8K | 794 tok/s | 1,243 tok/s | 57% |
| Qwen3.8 解码 | 57.4 tok/s | 58.9 tok/s | 约 3% |
其中,32K 指的是约 32,000 个 Token 的输入内容。
按测试吞吐量简单计算,处理同样长度的提示词:
- 旧版本需要约 41 秒。
- 新版本只需要约 22 秒。
一次节省约 19 秒。
如果你经常让本地 AI 阅读长文档、分析代码,或者使用 Agent 处理大量工具返回的信息,这种提升很有意义。
这次优化主要来自 AMD Strix Halo 的矩阵计算核心。Strata 默认启用了新的 F16 Matrix Core 内核,以及用于专家选择的矩阵核心计算路径。
NVIDIA 显卡也提速了,8 路并发提升 45%
开发者使用一张 RTX PRO 5000,在 Linux 下运行 Qwen3.8 IQ3_S,在同时处理 8 个请求时,并发性能提升约 45%,单个请求的速度也提升了约 5.3%。
另外,Strata 还为 Linux 上的 NVIDIA 显卡默认启用了 Gated DeltaNet 的 Tensor Core 计算内核。
根据测试:
- RTX PRO 5000:长提示词处理提升约 4%~6%。
- RTX A4000:16K 和 32K 提示词处理分别提升 2.6% 和 3.2%。
Intel Arc 显卡也有优化
Strata 0.2.9 还继续优化了 Intel Arc B 系列显卡。
- 在两张 Intel Arc Pro B65 的测试环境中,提示词处理速度提升了 4.6%~15.8%。
- 同时处理 8 个请求时,总生成吞吐量从 42.1 tok/s 提升到了 53.8 tok/s。
另外,Strata 现在可以自动识别并使用多张 Arc 显卡,在双 B65 的短提示词测试中,使用两张显卡的处理速度达到 439 tok/s,而单卡为 221 tok/s。
不过,这些优化也有代价:通过批处理服务器处理单个请求时,解码速度反而下降了约 2.7%。
新增 1-bit DeepSeek:下载就要 241GB

Strata 正式提供 Unsloth 的 1-bit DeepSeek 量化模型选项。
包括两个版本:
| 模型 | 下载体积 |
|---|---|
| UD-IQ1_S | 241GB |
| UD-IQ1_M | 258GB |
1-bit 量化的意义,普通电脑真的可以运行了,可以运行了。
开发者还提供了 61 项任务测试:
- UD-IQ1_S:完成 53 项。
- UD-IQ1_M:完成 53 项。
- ISTA-DAS 2.25:完成 56 项。
这个成绩略低于作为对照的 ISTA-DASLab 2.25-bit DeepSeek 量化版(56 项)。
多张 AMD 显卡终于可以自动一起工作
0.2.9 修复了多张 AMD 显卡不能同时工作的问题。
开发者使用四张 Radeon AI PRO R9700 测试 DeepSeek,更新后无需手动指定 GPU,就可以自动使用全部四张显卡。
原先默认只用一张卡时,短回答的解码速度为 7.8~25.7 tok/s。
更新后使用四张卡,六次聊天和工具调用回答的解码速度达到 57.7~103.7 tok/s。
聊天界面也变流畅了
另外 Strata 还修复了它提供的那个 Web 聊天界面,面对超长回答时,不再那么卡顿了。
在一段 40,000 Token 的模拟长回复测试中:
- 主线程耗时从 15.7 秒降至 9 秒。
- 峰值内存从 114MB 降至约 62~75MB。
此外,这次还修复了部分工具调用识别错误、Qwen3.6 预热阶段的 KV Cache 越界,以及 Windows 环境中非 UTF-8 输出导致安装或启动失败的问题。
青小蛙觉得
Strata 堪称那个能让每个人都有机会 Token 自由的项目,无论如何在2026年,只要你家里还有一台电脑,都推荐试试。
它已经傻瓜到只需要双击运行就行了。
项目地址:GitHub
原文:https://www.appinn.com/strata-0-2-9/
相关阅读
- Strata 现已支持 macOS,还能运行更轻量的 Qwen3.6-35B-A3B 大模型了
- 卷起来了!昨天 Strata 刚升级,今天 Splash 1.4 就宣布快了 30%
- Strata 0.2.8 发布:取消默认开启的 12GB 及以下显存加速功能,RTX 3060 生成代码快了 37%|支持 NVIDIA DGX Spark
- Strata 0.2.2 发布:新增多显卡自由分工,Gyro 推理提速最高 30%
- Strata 0.2 测试版发布:性能升级,门槛再降低|低配电脑运行本地大模型
©2021 青小蛙 for 小众软件 | 加入我们 | 投稿 | 订阅指南
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[分享创造] 大家用 DSH 这么久,不觉得插件一多左边栏就很乱吗?
大家用 DSH 这么久,不觉得插件一多,左边栏就很乱吗!最重要的对话区域也太少了!
我实在受不了,自己做了个插件解决这个问题:把全局导航收进左边一条窄栏,工作区和会话列表留在旁边。鼠标移过去就能展开导航,展开时不会挤占对话区。还可以改成点击展开,调整布局,加时钟和插件小组件。

安装后重启 DSH:
dsh plugin --profile web add dsh-better-left-sidebar
项目地址: https://github.com/exoticknight/dsh-better-left-sidebar
如果帮到你,请点点 Star ~
[程序员] 压缩的尽头是自指?从 LZMA2、PAQ8PX 到 zstd/ZIP/RAR Quine,我折腾了一遍“自包含压缩包”
最近把之前关于压缩的各种折腾整理成了一篇长文,发在博客上,想搬来论坛和大家聊聊。
一开始只是想给博客离线包找更强的压缩算法:7-Zip 的 LZMA2 已经很能打,但我不信邪,又试了 ZPAQ 、PAQ8PX 、NNCP 、cmix 。结果挺有意思:
- ZPAQ:一分钟左右把 16MiB 压到约 3.5MiB ,老算法但速度和压缩比还挺平衡;
- PAQ8PX:压到约 3.1MiB ,本次冠军,但花了一个半小时、吃了 30GiB 内存,解压同样痛苦;
- NNCP:FFmpeg 作者 Fabrice Bellard 的 Transformer 压缩算法,RTX 4090 上跑了一个半小时,结果 4.1MiB ,还没 LZMA2 高;
- cmix:测试时压到一半崩溃,内存和耗时也很夸张。
结论大概是:追求极限压缩比,PAQ8PX 很强;日常使用,LZMA2 的 7-Zip 依然是最稳的选择;想在小文件上再挤一点,ZPAQ 可以试试。
更有意思的是后半篇:继续折腾“自包含压缩包 / Quine 压缩包”——也就是压缩包里包含一份和自身字节完全相同的副本。之前做过 7z Quine ,这次又让 AI 研究了:
- zstd Quine:利用可跳过帧、RAW block 和 XXH64 校验值构造,最终做成
.tar.zst,Windows 资源管理器也能直接打开;文章里附了完整生成代码; - ZIP Quine:利用成员数据重叠绕过窗口限制,7-Zip 和 libarchive 能正常解压,但 Python
zipfile会因为检查压缩数据越过后续局部头而报错,算是一点兼容性代价;文章里也附了生成器; - RAR Quine:原理上也能做,AI 也生成出来了,但因为 RAR 不是开源软件,文章里只放了生成后的样例供研究。
文章里还有一段让我挺感慨的:这些代码我基本看不懂,但 AI 就是能把问题解决。复杂格式、校验值构造、兼容性坑,全都硬啃下来了。于是最后也忍不住写了点关于 AI 和“记录还有没有意义”的感想。
原文在这里:《关于压缩的各种探索》
感兴趣的朋友可以聊聊:
- 你用过最离谱但确实有效的压缩算法是什么?
- 极限压缩比和可用性之间,你会怎么选?
- 对这种“压缩包包含自身”的 Quine 玩法,有没有其他格式可以继续挑战?
- AI 生成这种复杂但能跑的代码,你觉得是解放生产力,还是让人越来越看不懂基础设施?
欢迎拍砖、补充算法和格式,也欢迎试试文中的 zstd/ZIP 生成器~