TwelveLabs video understanding models are now available in Amazon Bedrock

AWS adds native video embeddings and video understanding models to Amazon Bedrock. It opens a lot of potential use cases for which I previously reached for Gemini models. One example of such a case is an educational system that watches how the learner performs the task and provides feedback based on the educational materials.

Bedrock had workflows to do video understanding, but it was exactly that: workflows, not native models. You can imagine what they looked like—take a video, split to frames, feed frames to VLM, try to maintain temporal consistency, despair, come to terms with the system’s performance, and go on vacation.

Now, however, there are not one, but two different native video models:

1. TwelveLabs Marengo, for creating video embeddings;
2. TwelveLabs Pegasus, for video-based text generation.

Pricing of the models depends on whether your video has an audio track or not, but you should expect $2.5-$3/hour of video for Marengo and $1.8/hour for Pegasus.

One form of context rot is what I call self-reinforced structure. When you accept a long-form model answer, you signal that this structure is acceptable, and so it tries to generate subsequent responses in a similar way. It can be destructive for any long-form creative work.

The only real defense is ensuring that the history the model receives doesn’t contain such replies. So it should be either prevented early or later fixed by providing a summary of the previous conversation instead of the actual history.

Introducing ChatGPT agent: bridging research and action

OpenAI released an agent that can control your own computer. It uses its advanced reasoning capabilities to plan and solve tasks in applications like Excel and PowerPoint.

While Sam Altman “feels the agi” looking at how the system works, I find it incredibly clunky. Instead of concentrating on providing the models with native tools (MCP is a good step forward, although not without its problems), they try to emulate hands and eyes for them, so models can do the same things we do, but slowly and awkwardly.

So I would consider this type of agent a temporary workaround until we develop better machine-to-machine communication mechanisms. After this, it will be used to serve an increasingly long tail of legacy systems that will not have such machine-usable interfaces.

P.S. Gemini mentioned a point of view I didn’t consider, namely that such systems can collect data necessary to train better embodied intelligence, meaning one that can act in the real world. It’s a perfectly valid point that shouldn’t be left without attention.

Stanford’s 2025 AI Index Report

Stanford published its annual report. It’s pretty important, because it separates speculation from pure numbers. Along with some obvious things (AI is getting better, cheaper, widespread, duh), there are some very interesting facts:

1. While almost every organization is using AI now (78% in 2024, although no doubt, for most of them it boils down to using chatbots to compose emails), the actual results are somewhat modest. The productivity increase is on the scale of 10% (to be honest, such an increase in one year is kinda unprecedented), but the increase in revenue for most industries is just about 5%. Why? Because as with any general purpose technology, realization of full benefit would require complete rebuilding the organizational structures and processes. The problem is that no one knows how these new processes would look like, and we will have to learn from our own mistakes.

2. Maybe old news, but AI provides more leverage to less experienced employees. The great equalizer of modern times. Again, that means that we need to reformulate our approach to team staffing. I would only add that it can help only if you have some remote understanding of what you’re doing, so those who apply for entry positions, do your homework well.

3. The number of AI-related incidents continues to rise. We see a twofold increase in 2024 vs 2023, and this is before frantic adoption of Agents and MCPs we see in 2025. So, we need to brace ourselves and be ready for more and more data leaks and integrity breaches.

The report contains a lot more nuggets, but it’s almost 500 pages long, so I would really recommend to use AI to extract what you fancy.

Voxtral

Mistral introduces Voxtral, a family of open-source speech recognition and understanding models. It’s about time. We haven’t seen a comparable open-source model since OpenAI’s Whisper, and that was quite a while ago.

The models are provided in 3B and 24B sizes and outperform Whisper on most benchmarks. However, they require more powerful hardware, as the largest Whisper variant is just 1.5B. This is a direct consequence of it also being a regular language model. Another consequence is that controlling them in a pure transcription setting would be harder.

The models are available on Hugging Face as well as through the Mistral API and their LeChat.

What they also currently lack is diarization (speaker recognition) support. It’s on the roadmap, but in the meantime, we still have to use somewhat clunky pyannote-audio for this purpose.

Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory

The paper shows that different models behave completely differently when placed in game theory settings. What that means is that testing and evals are playing an increasingly critical role in developing agentic systems, as updating or changing the underlying model will lead to unpredictable changes in an agent’s behaviour.

Introducing Kiro

AWS jumps into the agentic IDEs bandwagon with Kiro. To separate itself from vibe-coding approach, which accumulated a considerable amount of ill repute, they emphasize the “spec-driven development” method. That means that the agent first helps the user to create a full requirements document for the feature, then it analyzes the existing code base, and only after that it starts implementing.

This approach definitely makes sense, and it’s a step forward from blindly running into the fray that is vibe-coding. The fact that those specs are updating along with the code changes makes them even more valuable, minimizing the problem of stale documentation. Hooks can run repeated agentic tasks, such as making sure the new feature has sufficient tests, automatically.

It is interesting to watch how different tools adopt different methodologies, as it allows the developers community to find and disseminate the techniques that really work.

TIL: ccusage — a nice tool to track and analyze the Claude Code usage.

Anthropic released 4 new courses in its academy:

1. Claude Code in Action with practical advice on using the CLI agent.
2. Claude with the Anthropic API, a comprehensive course on using all current API capabilities, from single-shot text generation to agents.
3. Introduction to Model Context Protocol and Model Context Protocol: Advanced Topics for those interested in MCP.

Each course comes in video and text formats and provides a certificate of completion.

TIL: DBML - Database Markup Language

A markup language for DB schema description that can be useful to provide it to AI tools.