Thomas Koller, PhD

Polyglot keen on NLP, data analysis/mining, machine learning, deep learning, computer vision, forecasting, R, Python, (No)SQL, Neo4j, graph databases, hackathon

[Thomas Koller, PhD @ThomasKoller]

How to choose algorithms - Azure Machine Learning Studio https://t.co/vhEZuBdnDi

[Thomas Koller, PhD @ThomasKoller]

@_onlyscott Bernd Schuster

[Vaibhav Sisinty @VaibhavSisinty]

It's been 8 hours since Claude Fable 5 launched.

Here are the wildest things people have already built with it. 🤯

🧵

[Matthew Prince 🌥 @eastdakota]

Welp, that happened faster than I predicted. Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet's history. https://t.co/2zX5bHdhsa

[Rahul @sairahul1]

https://t.co/4U0aNsoAmo

[Archive @ArchiveExplorer]

"Claude usage limit reached. Your limit will reset at 7pm"

every. fucking. day.

was about to pay $200 for Max. then I read this article

98.5% of tokens - wasted

you're not paying for answers. you're paying for Claude to re-read its own homework 30 times

spent months blaming Anthropic for being greedy. turns out the problem was how I write prompts

5 minutes of reading

basic plan now handles more than my old Max

[Nav Toor @heynavtoor]

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars.

It's called Goose.

An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose.

Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer.

No vendor lock-in. No monthly subscription. Bring your own model.

Here's what Goose does:

→ Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice.
→ Reads and understands your entire codebase
→ Writes, edits, and refactors code across multiple files
→ Runs shell commands and installs dependencies
→ Executes and debugs your code automatically
→ Extensible through MCP. Connect it to any external tool.
→ Desktop app, CLI, and web interface. Pick your workflow.
→ Written in Rust. Fast. Lightweight. No bloat.

Here's the wildest part:

Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing.

This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone.

Claude Code: $200/month. Locked to Claude.
GitHub Copilot: $19/month. Locked to GitHub.
Cursor: $20/month. Locked to their editor.

Goose: Free. Any LLM. Any editor. Any workflow. Forever.

35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block.

100% Open Source. Apache 2.0 License.

[Paul Couvert @itsPaulAi]

Friendly reminder that Google has an official app to run Gemma 4 on your phone.

- 100% open source
- Fully offline and private
- Multimodal with text/audio/image
- Works with Gemma E4B and E2B

And the app is available on both iOS and Android.

Steps and download below

[Min Choi @minchoi]

Less than 48 hours ago, Google dropped Gemma 4.

Minds are blown. And people are already coming up with wild use cases.

10 examples:

[ollama @ollama]

ollama run translategemma

TranslateGemma is available on Ollama. Now you can use it in apps to translate between 55 languages.

Note, it requires a specific prompting format 👇👇👇

[Shraddha Bharuka @BharukaShraddha]

Google isn’t trying to win the AI race.

They’re trying to own the entire AI Agent ecosystem.

While everyone argues ChatGPT vs Claude, Google quietly built:

Models → Gemini Pro, Flash, Deep Think, Gemma

Design → Stitch, Whisk, Imagen

Research → NotebookLM, AI Mode

Video → Veo, Flow, Google Vids

Coding → Antigravity IDE, Gemini CLI, Jules

Agents → A2A, ADK, FileSearch API

The scary part?

All of these tools talk to each other.

That means:
10x faster prototypes
End-to-end AI workflows
Production-ready agents on GCP

The next AI war won’t be model vs model.
It’ll be ecosystem vs ecosystem.

Save. Share. Build.

[Patrick Loeber @patloeber]

We shipped a Gemini Docs MCP🚢

Now you can set up your coding assistant with both MCP and Gemini Skills!

When building with the Gemini API, our internal tests yield 10X better results in coding outputs compared to not using them. Give it a try :)

[Base44 @Base44]

130+ skills are now built into your Superagent.

Some are ready to use, and some can be created based on what you need.
Add a skill once, and your Superagent can use it as part of your workflows.

Stack skills, connect tools, and build flows that run end-to-end.

[Alvaro Cintas @dr_cintas]

This is the most complete Claude Code setup that exists right now.
27 agents. 64 skills. 33 commands. All open source.

The Anthropic hackathon winner open-sourced his entire system, refined over 10 months of building real products.

What's inside:
→ 27 agents (plan, review, fix builds, security audits)
→ 64 skills (TDD, token optimization, memory persistence)
→ 33 commands (/plan, /tdd, /security-scan, /refactor-clean)
→ AgentShield: 1,282 security tests, 98% coverage

60% documented cost reduction.

Works on Claude Code, Cursor, OpenCode, Codex CLI. 100% open source.

[Hugging Models @HuggingModels]

Ever wanted to clone a voice from just a few seconds of audio? Meet Qwen3-TTS, a text-to-speech model that can mimic voices with incredible accuracy. It's blowing up with nearly 1M downloads because it makes custom voice creation accessible to everyone.

[Mistral AI for Developers @MistralDevs]

🎙️Do you know you now have all the building blocks for full speech-to-speech?
- Voxtral Realtime: High-quality, real-time speech-to-text.
- Mistral Small 4: Fast, efficient, general-purpose agentic model.
- Voxtral TTS: Realistic customizable text-to-speech with streaming output.

[Mistral AI @MistralAI]

🔊Introducing Voxtral TTS: our new frontier open-weight model for natural, expressive, and ultra-fast text-to-speech

🎭Realistic, emotionally expressive speech.
🌍Supports 9 languages and accurately captures diverse dialects.
⚡Very low latency for time-to-first-audio.
🔄Easily adaptable to new voices

[Thomas Koller, PhD @ThomasKoller]

@MichLieben PROMPT

[Jasmin @AI_with_jasmin]

BREAKING: If you're not using Claude at your job, you're already behind.

Copy these 7 prompts:

[Alex Finn @AlexFinn]

This is potentially the biggest news of the year

Google just released TurboQuant. An algorithm that makes LLM’s smaller and faster, without losing quality

Meaning that 16gb Mac Mini now can run INCREDIBLE AI models. Completely locally, free, and secure

This also means:

• Much larger context windows possible with way less slowdown and degradation
• You’ll be able to run high quality AI on your phone
• Speed and quality up. Prices down.

The people who made fun of you for buying a Mac Mini now have major egg on their face.

This pushes all of AI forward in a such a MASSIVE way

It can’t be stated enough: props to Google for releasing this for all. They could have gatekept it for themselves like I imagine a lot of other big AI labs would have. They didn’t. They decided to advance humanity.

2026 is going to be the biggest year in human history.

[Vaidehi @Ai_Vaidehi]

This is one of those “this changes everything” moments.

Microsoft just broke a core assumption in AI.

You needed GPUs to run big models.

Not anymore.

They open-sourced BitNet,
an inference framework that runs a 100B parameter LLM on a single CPU 🤯

No GPU
No cloud
No expensive setup

Just your laptop.

Here’s the trick:

Instead of 16-bit or 32-bit weights...
BitNet uses 1.58 bits

Yes, seriously.

Weights = -1, 0, +1

That’s it.

No heavy matrix math.
Just simple integer ops your CPU already handles easily.

And the results?

• 100B model → 5–7 tokens/sec on CPU
• Up to 6x faster than llama.cpp
• 82% less energy usage
• Runs on x86 + ARM (MacBook)
• Memory reduced by 16–32x

But here’s the insane part:

👉 Accuracy barely drops.

Their model (BitNet b1.58 2B4T) competes with full-precision models trained the “normal” way.

So what does this unlock?

• Fully offline AI (privacy ↑)
• No more API bills
• AI on phones, IoT, edge devices
• Access in low-internet regions

We’re watching AI move from
“cloud-only” → “runs anywhere”

The GPU monopoly just got… shaky.

And this is open source.

Let that sink in. 🚀