Agent decided to test if harmful command block worked by issuing a rm -rf /
Thankfully it worked so only damage was a mild heart attack.
I implemented a sandbox immediately afterwards.
EDIT: for those wondering, I was implementing a bash command whitelist and also bubblewrap for isolation. I did the whitelist implementation first and that was the command the agent chose to test it 😂 bwrap got done quickly afterwards!
A month ago, I experimented with a very basic home-rolled agent loop with a handful of tools and found it worked surprisingly well in spite of how crude it was:
Later, I wrote about how I addictive developing your own agent loop is, esp. once you reach the point that the agent loop is capable of editing itself:
https://www.reddit.com/r/LocalLLaMA/comments/1sq7cie/warning_do_not_write_your_own_ai_agent_if_you/
Well, 28 days later, it's been getting out of hand. I've been working until 5am on it as it was so addictive.
Once you have a good agentic setup, you quickly realise that you, as the human, is the main bottleneck. You have a massive todo list, but the agent is sitting idle, waiting your your approvals and reviews.
Not only that, since I am using Qwen3.5 9B as the model, the model has limited intelligence and context. I can't just dump hundreds of data files onto it and expect it to crunch it all at the same time, so then I thought to manage the context limits through a map-reduce pattern, breaking tasks down into smaller chunks that can be run in parallel to extract maximum FLOPs out of the GPU while staying within context limits.
Enforcing structured outputs also helps to reduce LLM variability and make a smooth reduce step.
Lastly, it is helpful to have a database to monitor and track workflows. Managed to get it up and running today and happy that small local models can handle this task.
My custom agent has now replace Claude Code for 99% of tasks. The agent isn't released yet, but I hope to open source at some point in the future.
I'm trying to learn German. I use Qwen3.5/3.6 locally, but this is pretty bad for German grammar. Has anyone got a recommendation for a small-ish local model that knows German grammer well and can answer questions on this?
EDIT:
I give an example output from unquantized Qwen3.5 9B. This starts off OK, then continues to become unnatural/ungrammatical and ends up contradictory/wrong:
>Considering only the subordinate clause introduced by "wenn", there are indeed other orderings permitted, though they will change the emphasis or flow of the sentence.
>
>The core rule is that the conjugated verb (würde) must remain at the end of the clause. Everything else can be rearranged to some degree.
>
>Here are the main variations:
>
># 1. Standard Order (Subject-Verb-Object)
>
>This is the most common and neutral order.
>
>* Structure: Wenn + Subject + Object + Verb + Comparison
>* Sentence: Wenn mein Freund so viele Versprechen brechen würde wie der Kanzler...
>* Meaning: If my friend were to break as many promises as the Chancellor...
>
># 2. Object-First (Topicalization)
>
>You can move the object (so viele Versprechen) to the very beginning of the clause. This places emphasis on the "promises".
>
>* Structure: Wenn + Object + Subject + Verb + Comparison
>* Sentence: Wenn so viele Versprechen mein Freund brechen würde wie der Kanzler...
>* Meaning: If so many promises my friend were to break as the Chancellor... (emphasizing the quantity of promises)
>
># 3. Comparison-First (Fronting the "wie" phrase)
>
>You can move the comparison phrase (wie der Kanzler) to the beginning of the clause. This is a bit more dramatic and emphasizes the comparison itself.
>
>* Structure: Wenn + Comparison + Subject + Object + Verb
>* Sentence: Wenn wie der Kanzler mein Freund so viele Versprechen brechen würde...
>* Meaning: If, like the Chancellor, my friend were to break so many promises... (emphasizing the "like the Chancellor" part)
>
># 4. Separating the Verb
>
>The verb phrase brechen würde can be separated if you insert another element between the infinitive (brechen) and the auxiliary (würde). This is less common but grammatically possible.
>
>* Structure: Wenn + Subject + Object + Infinitive + Comparison + Auxiliary
>* Sentence: Wenn mein Freund so viele Versprechen wie der Kanzler brechen würde...
>* Meaning: If my friend were to break as many promises as the Chancellor...
>* Note: This is actually a very natural and common way to say it, where the comparison phrase is placed before the verb.
>
># Summary of Permitted Orders (Verb at the end):
>
>|Order|Example|Emphasis|
>|:-|:-|:-|
>|Standard|Wenn mein Freund so viele Versprechen brechen würde wie der Kanzler|Neutral|
>|Object-First|Wenn so viele Versprechen mein Freund brechen würde wie der Kanzler|On the "promises"|
>|Comparison-First|Wenn wie der Kanzler mein Freund so viele Versprechen brechen würde|On the "like the Chancellor" part|
>|Comparison-Mid|Wenn mein Freund so viele Versprechen wie der Kanzler brechen würde|Natural, slightly different flow|
>
>Key Takeaway: As long as the conjugated verb (würde) remains at the very end of the subordinate clause, you have significant freedom to rearrange the other elements for stylistic effect.
>Intel and AMD have jointly unveiled AI Compute Extensions (ACE), a new x86 instruction set extension designed to revolutionize CPU-based artificial intelligence processing. Developed under the x86 Ecosystem Advisory Group (EAG) to prevent the fragmentation that historically plagued industry standards like AVX-512, ACE introduces specialized 2D tile registers and outer-product algorithms capable of performing 1,024 multiplications per clock cycle—compared to just 64 for traditional AVX instructions. This architectural shift effectively delivers a massive 16x increase in compute density over existing AVX10 technology by enabling simultaneous matrix operations directly on the CPU, bringing GPU-like tensor core capabilities to standard processor architectures while maintaining full backward compatibility. > >The implications of this unified standard are profound for both energy efficiency and software scalability across the computing ecosystem. By allowing lightweight AI workloads to execute directly on CPUs with significantly lower power consumption than GPUs, ACE addresses critical bottlenecks in data center energy usage and latency. Furthermore, the collaborative approach ensures that optimized kernels and libraries for major frameworks like PyTorch, TensorFlow, NumPy, and SciPy will run consistently without modification across Intel and AMD hardware, from consumer laptops to enterprise servers. While no hardware supporting ACE has been released yet, this move establishes a robust foundation for seamless AI deployment, potentially redefining how general-purpose processors handle machine learning tasks in the coming years.