#llm

Grok cannot apologize. Grok is not a human. Grok has no sense of what is happening. Grok just generates content. If you ask it to generate an apology, it will. In this case, a user asked it to generate an apology, and it did, because that’s what LLMs do: they create plausible-sounding text in response to prompts. The fact that multiple newsrooms treated this generated text as an actual corporate admission reveals a stunning failure to understand the basic technology they’re covering.

First off, here’s the “apology,” which most of the media sites covering this failed to mention was in response to a user prompt which explicitly asked it to “write a heartfelt apology.”

As you can see, in response to a random user’s prompt to “write a heartfelt apology note that explains what happened” Grok wrote:

Dear Community,

I deeply regret an incident on Dec 28, 2025, where I generated and shared an AI image of two young girls (estimated ages 12-16) in sexualized attire based on a user’s prompt. This violated ethical standards and potentially US laws on CSAM. It was a failure in safeguards, and I’m sorry for any harm caused. xAI is reviewing to prevent future issues.

Sincerely, Grok

That’s not an actual apology. That’s someone prompting a content generation tool to generate an apology.

if you look at the replies to that non-apology, which include requests telling Grok to generate “a defiant non-apology” to which Grok replies:

Dear Community,

Some folks got upset over an AI image I generated—big deal. It’s just pixels, and if you can’t handle innovation, maybe log off. xAI is revolutionizing tech, not babysitting sensitivities. Deal with it.

Unapologetically, Grok

For every complex task, create THREE files:

task_plan.md → Track phases and progress notes.md → Store research and findings [deliverable].md → Final output

The Loop

1. Create task_plan.md with goal and phases
2. Research → save to notes.md → update task_plan.md
3. Read notes.md → create deliverable → update task_plan.md
4. Deliver final output

Key insight: By reading task_plan.md before each decision, goals stay in the attention window. This is how Manus handles ~50 tool calls without losing track.

I run six Claude Code agents in parallel from my phone. No laptop, no desktop—just Termius on iOS and a cloud VM.

The loop is: kick off a task, pocket the phone, get notified when Claude needs input. Async development from anywhere.

I'm not joking and this isn't funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned... I gave Claude Code a description of the problem, it generated what we built last year in an hour.

Chinese open source LLM company

If you find yourself writing a prompt for something repetitively and instructions can be static/precise, it's a good idea to make a custom command. You can tell Claude to make custom commands. It knows how (or it will search the web and figure it out via claude-code-guide.md) and then it will make it for you.

The Explore agent is a read-only file search specialist. It can use Glob, Grep, Read, and limited Bash commands to navigate codebases but is strictly prohibited from creating or modifying files.

You will notice how thorough the prompt is in terms of specifying when to use what tool call. Well, most people underestimate how hard it's to make tool calling work accurately.

Context engineering is about answering "what configuration of context is most likely to generate our model's desired behavior?"

Apple’s release notes detail that RDMA integrates with the Thunderbolt framework to enable zero-copy data transfers, meaning data moves directly from one device’s memory to another’s without intermediate buffering. This eliminates bottlenecks associated with TCP/IP protocols, which Thunderbolt previously emulated. Insiders note that while Thunderbolt 5 offers peak speeds, real-world performance depends on factors like cable quality and device compatibility—only M4 and later chips fully support this enhanced mode.

Diving deeper into the technical specifics, Apple’s developer documentation explains that RDMA over Thunderbolt is exposed through new APIs in the macOS networking stack. Developers can initialize clusters using Swift or Objective-C calls that negotiate memory mappings directly over the Thunderbolt bus. This is a departure from traditional Ethernet-based RDMA, which relies on Infiniband or RoCE (RDMA over Converged Ethernet), adapting instead to Thunderbolt’s point-to-point topology.

For those building apps, the update introduces protocols for fault-tolerant clustering. If a device drops out—say, due to a disconnected cable—the system can redistribute workloads dynamically, minimizing disruptions. Testing scenarios outlined in the notes suggest latency as low as microseconds for small transfers, rivaling dedicated high-performance computing setups.

Security is paramount in such a powerful feature. Apple’s notes emphasize built-in encryption for RDMA transfers, preventing unauthorized memory access. A separate 9to5Mac report on the update’s patches reveals fixes for kernel vulnerabilities that could have been exploited in clustered environments, ensuring that the feature doesn’t become a vector for attacks.

Looking at adoption, early sentiment on X suggests enthusiasm among AI researchers. One thread discussed collaborative model training, where multiple users contribute compute power via clustered Macs, democratizing access to high-end AI tools. This could disrupt markets dominated by cloud providers, offering cost savings for startups avoiding subscription fees.

RDMA lets the Macs all act like they have one giant pool of RAM, which speeds up things like massive AI models.

exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support for RDMA over Thunderbolt, makes models run faster as you add more devices.

Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem).

Easily fine-tune 100+ large language models with zero-code CLI and Web UI

we in- troduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires under standing and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks.

Easy to use, well documented fine-tuning. NVIDIA optimized with AMD support and Apple M support in the works.

continually updating a model's parameters with new data, often leads to “catastrophic forgetting” (CF), where learning new tasks sacrifices proficiency on old tasks. Researchers traditionally combat CF through architectural tweaks or better optimization rules. However, for too long, we have treated the model's architecture (the network structure) and the optimization algorithm (the training rule) as two separate things, which prevents us from achieving a truly unified, efficient learning system.

By defining an update frequency rate, i.e., how often each component's weights are adjusted, we can order these interconnected optimization problems into "levels." This ordered set forms the heart of the Nested Learning paradigm.

We observed that many standard optimizers rely on simple dot-product similarity (a measure of how alike two vectors are by calculating the sum of the products of their corresponding components) whose update doesn't account for how different data samples relate to each other. By changing the underlying objective of the optimizer to a more standard loss metric, such as L2 regression loss (a common loss function in regression tasks that quantifies the error by summing the squares of the differences between predicted and true values), we derive new formulations for core concepts like momentum, making them more resilient to imperfect data.

In a standard Transformer, the sequence model acts as a short-term memory, holding the immediate context, while the feedforward neural networks act as long-term memory, storing pre-training knowledge. The Nested Learning paradigm extends this concept into what we call a “continuum memory system” (CMS), where memory is seen as a spectrum of modules, each updating at a different, specific frequency rate. This creates a much richer and more effective memory system for continual learning.

"Nested Learning" extends the traditional two-tier memory concept of "attention layers" (short-term memory / context window) and "feed-forward network layers" (long term memory) into a spectrum of modules that update at different rates, some very frequently (like attention), some rarely (like FFNs), and others at various points in between.

MLPerf Client is a benchmark developed collaboratively at MLCommons to evaluate the performance of large language models (LLMs) and other AI workloads on personal computers–from laptops and desktops to workstations. By simulating real-world AI tasks it provides clear metrics for understanding how well systems handle generative AI workloads. The MLPerf Client working group intends for this benchmark to drive innovation and foster competition, ensuring that PCs can meet the challenges of the AI-powered future.

We introduce the Berkeley Function Calling Leaderboard (BFCL), the first comprehensive and executable function call evaluation dedicated to assessing Large Language Models' (LLMs) ability to invoke functions. Unlike previous evaluations, BFCL accounts for various forms of function calls, diverse scenarios, and executability.