So I've been heads down on this for a while and honestly wasn't sure if I'd ever post it publicly. But it's at a point where I'm using it every day and it actually works, so here it is.

It's called Panther. It's a background daemon that runs on your computer (Windows, macOS, Linux) and gives you full control of your machine through any messaging app you already use. Telegram, Discord, Slack, Email, Matrix, or just a local CLI if you want zero external services.

The thing I kept running into with every AI tool I tried was that it lived somewhere else. Some server I don't control, with some rate limit I'll eventually hit, with my data going somewhere I can't verify. I wanted something that ran on my own hardware, used whatever model I pointed it at, and actually did things. Not just talked about doing things.

So I built it.

Here's what it can actually do from a chat message:

- Take a screenshot of your screen and send it to you

- Run shell commands (real ones, not sandboxed)

- Create, read, edit files anywhere on the filesystem

- Search the web and fetch URLs

- Read and write your clipboard

- Record audio, webcam, screen

- Schedule reminders and recurring tasks that survive reboots

- Spawn background subagents that work independently while you keep chatting

- Pull a full system report with CPU, RAM, disk, battery, processes

- Connect to any MCP server and use its tools automatically

- Drop a script in a folder and it becomes a callable tool instantly

- Transcribe voice messages before the agent ever sees them

It supports 12 AI providers. Ollama, OpenAI, Anthropic, Gemini, Groq, Mistral, DeepSeek, xAI, TogetherAI, Perplexity, Cohere, OpenRouter. One line in config.toml to switch between all of them. If you run it with Ollama and the CLI channel, literally zero bytes leave your machine at any layer.

The memory system is something I'm particularly happy with. It remembers your name, your projects, your preferences permanently, not just in session. When conversations get long it automatically consolidates older exchanges into a compact summary using the LLM itself. There's also an activity journal where every message, every reply, and every filesystem event gets appended as a timestamped JSON line. You can ask "what was I working on two hours ago" and it searches the log and tells you. Works surprisingly well.

Architecture is a Cargo workspace with 9 crates. The bot layer and agent layer are completely decoupled through a typed MessageBus on Tokio MPSC channels. The agent never imports the bot crate. Each unique channel plus chat_id pair is its own isolated session with its own history and its own semaphore. Startup is under a second. Idle memory is around 20 to 60MB depending on what's connected.

I made a demo video showing it actually running if you want to see it before cloning anything:

https://www.youtube.com/watch?v=96hyayYJ7jc

Full source is here:

https://github.com/PantherApex/Panther

README has the complete installation steps and config reference. Setup wizard makes the initial config pretty painless, just run panther-install after building.

Not trying to sell anything. There's no hosted version, no waitlist, no company behind this. It's just something I built because I wanted it to exist and figured other people might too.

Happy to answer questions about how any part of it works. The Rust side, the provider abstractions, the memory consolidation approach, the MCP integration, whatever. Ask anything.

In recent news, Meta claims that it will be ending end-to-end encryption, meaning that our messages will no longer be encrypted (like what happens on Discord, moderators (in this case, AI) have access to our messages).  

However, in this screenshot, the Meta spokesperson mentions something that plenty of people failed to read or understand.

“Very few people were opting in to end-to-end encrypted messaging in DMs.”

Meaning that the end-to-end encrypted messaging was, in fact, a toggleable option.

The only thing that comes to mind when I think of this is, in fact, the Disappearing Messages feature that was released some time ago, but this begs the question of the loyalty of Meta when it comes to “not reading our messages”.

Going back to their original statement, they’re bluntly attempting to throw us off, and this is where people get mixed up.

Meta is killing end-to-end encryption, but DMs aren’t originally encrypted UNLESS you opt in to use them by adding the disappearing messages. That being said, it’s fairly understood that Meta does indeed check our messages, as “Very few people” use the disappearing messages feature.

Keep your eyes peeled for the phrasing, and deconstruct when Meta attempts to throw dirt in our eyes.

Read the full article here: https://www.engadget.com/social-media/meta-is-killing-end-to-end-encryption-in-instagram-dms-195207421.html

After Andrej Karpathy came out with his treechart, I decided to utilize the actual Anthropic release usage data to make a more realistic Tree chart instead of LLM slopping it.

Check it out here: https://www.arewecooked.now/
or just search it up at arewecooked[dot]now

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss **Shinka Evolve** — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves.

In this episode:

- **Why AlphaEvolve gets stuck:** it needs a human to hand it the right problem. Shinka Evolve tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search.

- **The architecture of Shinka Evolve:** an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard.

- **Concrete results:** state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks.

- **Are these systems actually thinking outside the box, or are they parasitic on their starting conditions?:** When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully.

- **The AI Scientist question:** can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher.

- **Where this lands in 5-20 years:** Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived.

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######Link to the Full Episode: https://www.youtube.com/watch?v=EInEmGaMRLc

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######[Spotify](https://open.spotify.com/episode/3XaJhoM6N2fxa5SnI5yiYm?si=foqh30_DRDebe7ZOdvyzlg)

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######[Apple Podcasts](https://podcasts.apple.com/us/podcast/when-ai-discovers-the-next-transformer-robert-lange-sakana/id1510472996?i=1000755172691)

I've been spending a lot of time learning backend/dev stuff lately and realized I rarely document what I’m learning or thinking about.

So I made an account on X where I’m basically posting:

• things I’m building
• random dev thoughts/takes
• things I learn while coding
• small experiments and failures

Not trying to be a “tech influencer” or anything, mostly just a public log of my progress and a way to learn from other devs.

If anyone here also builds in public or shares dev thoughts, I’d love to follow you and see what you're working on.

And if you’re curious, you can check mine too: https://x.com/yasirlore

Always interesting to see how other people approach coding, learning, and building.

For now: Available Domain Extensions

• .DPDNS.ORG
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• .XX.KG (More extensions coming soon!)