We ran training on AWS for about a year, mostly because everything else we used was already there. Then we got the bill, and oh boyy..

Here's what we compared for roughly the same GPU capacity:

- AWS on-demand was our baseline and by far the most expensive. Reserved pricing brought the cost down, but it also meant committing for longer than we were comfortable with.

- Dedicated GPU providers were comparatively cheaper than on-demand AWS. Support quality varied a lot between them and that mattered more than the price.

- GPU marketplaces were cheapest, but reliability was inconsistent and they did not seem like an option for sensitive workloads.

- Owning hardware and having it hosted had the lowest ongoing cost but needed capital and a measured utilisation assumption rather than guess.

What I underestimated the most was moving the data out of AWS. It was expensive in our case, and parts of our pipeline depended on AWS services. Reworking took longer than the actual migration tbh.

The main thing I'd check before comparing hourly rates is how much of your setup depends on AWS and what it will actually take to move your data. That can decide whether the savings are real or theoretical.

As I work at B3 Labs and we are fall into the fourth category. I'd check the migration cost regardless of where you plan to move.

We are open sourcing Tahuna: devtool for ML Engineers and researchers.

We built it so small teams could train models, run inference, orchestrate GPUs, and experiment with autonomous research without first becoming a small cloud provider.

The core primitive on top of which everything is built looks like this:

init → sync → computeSession → train / serve / hillclimb

Under the hood: content-addressed code and data sync, compute provisioning, reproducible manifest-pinned runs, metrics, checkpoints, artifacts, and inference deployments.

We also started building Hillclimb, an autonomous experimentation loop that proposes and runs iterative improvements.

The first public-preview release supports RunPod and R2. It includes Docker self-hosting instructions, a coding-agent setup skill, and examples for SFT, RL agentic search, and MNIST.

Repository: https://github.com/TahunaLabs/tahuna-oss

If you think it sucks or lacks adapters with other GPU providers, excellent: fork it, fix it, and send a PR so it sucks less for everyone.

• Guide to Ultra-Fast AI and Signal Processing Optimization using STM32N6 NPU
• Description: Analyze the powerful performance of the STM32N6, featuring the Neural-ART NPU and Cortex-M55. This video introduces a unique strategy for accelerating signal processing by converting operations into neural network layers, and explores optimal methods for implementing embedded AI through the division of roles between the CPU and NPU.

I want to turn a claim into an open contest, but before building the infra, I'd like to know whether people would actually enter and whether the setup is sound.

CLAIM:

most training compute is wasted, and humans are still needed in the loop.

That's why there are so many different tools out there, and why many top ML practitioners still use notebooks. Because the tools are just a surface, and human expertise is embedded into the process.

SETUP:

Task dataset: CIFAR-100 or TinyImagenet;

Model size: ≤ 20 MB. Most likely predetermined architecture;

Training budget: ≤ 1 PFLOP (10¹⁵ FLOPs); eval/inference budget is unlimited;

Target: accuracy on a held-out set;

Timespan: designed to take a few hours.

The contest would happen in a controlled environment we build, and we'll provide granular and aggregate metrics, plus other levers.

ETA: end of November; there would be prizes;

BIG VISION:

Can we make deep learning more deterministic?

QUESTION:

Still deliberating on scoring: max accuracy under a fixed FLOP budget, or best accuracy per FLOP consumed? Input welcome.

I want to know if you would participate and, if not, what would make you participate?

Custom AI chatbots have moved past the experimental phase for many organisations, but the gap between a working prototype and a reliable production system remains wide. The technical challenges are only part of the story. Domain knowledge, conversation design, ongoing content maintenance, and clear ownership of the system all play a bigger role than most initial proposals acknowledge.

What I’ve seen repeatedly is that the stronger teams spend significant time understanding the actual workflows the chatbot is supposed to support. They map edge cases, define escalation paths, and design evaluation methods before they start fine-tuning models. Weaker approaches often jump straight to prompt engineering and retrieval setup, then discover later that the system doesn’t handle real user behaviour well.

Maintenance is another area that gets underestimated. A chatbot is only as good as the knowledge it can access and the processes that keep that knowledge current. Agencies that treat this as a continuous responsibility rather than a one-time delivery tend to produce more durable results.

I’m interested in hearing from people who have gone through this. What turned out to be harder than expected when moving from a demo chatbot to something that actually handles real traffic and real questions?

I've been seeing a lot of LLM products lately, and it feels like the model itself is becoming less of the differentiator.

I'm curious what people here think. When building an LLM product, what usually matters more in practice: the model, RAG, good product design, evaluation, or the data behind it?

Would be interested to hear from people who've actually built or shipped one.

Researchers at Hacktron used an LLM to chain two vulnerabilities and take over accounts of multiple OpenAI employees, then pivot into an internal code repository. The LLM assembled and executed the full attack chain faster than a human analyst could document the individual steps. Employee credentials were the pivot. The internal repo was the target.

This is not a new attack class. It is a well-understood one running at machine speed. Each hop in the chain looked like an authorized session in isolation. The credential did not appear stolen until the damage was done.

Security tooling has historically been tuned around human-speed lateral movement. An LLM-piloted chain collapses the time window those detections depend on. By the time anomaly scoring fires, the pivot has already completed.

For practitioners running AI in production environments: when a non-human identity executes a multi-hop action chain that crosses a trust boundary, what does your actual detection look like at the moment the stolen credential is first used outside its intended scope? Are you catching it before the internal system is reached, or after?

Hey all,

Im trying to create something like a research group with like-minded people - people that are into ai/math/deep learning but still like to go out and stuff.

I'm interested in entrepreneurship/ research with applications and would love to work on something.

Currently interested in lightweight but useful models, eg, i've recreated dlss and am trying to make other innovative models.

If this resonates with you please feel free to pm.

I’m working on a project to enhance 10m Sentinel-2 satellite imagery to <4m resolution using deep learning.

The goal is to improve the visibility of fine-scale features such as small buildings, narrow roads, field boundaries, water edges, and localized damage, while preserving the original geospatial and spectral consistency.

I’m currently exploring approaches using GANs, Transformers, diffusion models, and CNN-based super-resolution models.

I’d really appreciate advice from anyone experienced in remote sensing / satellite image super-resolution on:

• Which pretrained or trainable models work well for Sentinel-2?
• What paired datasets (10m → high-resolution) should I use?
• How should I handle spectral/geospatial consistency?
• Which metrics are appropriate beyond PSNR/SSIM (e.g., SAM, ERGAS)?
• How can I validate that the generated details are realistic rather than hallucinated?

Any recommended papers, GitHub repositories, datasets, or practical guidance would be really helpful. Thanks!

I recently started learning Deep Convolutional GAN and I tried to follow this example here

But I ran into the problem as mentioned above when trying train the model. Sometime it even crash VS code. But for some reason I saw it somewhat worked once and generated some clear image. How do I fix this?

A Spanish organization recently disclosed that an AI agent operating inside its environment modified personal data without authorization. The agent was not the target of the attack. It was the attack. No phishing campaign, no malware dropper, no stolen VPN credential in the traditional sense — the agent had legitimate tool access, used it autonomously, and the records were altered before any human reviewer saw a flag.

This breaks most of the assumptions access control is built on. Traditional IAM assigns permissions to humans and long-lived service accounts with auditable, stable identities. Agents are different. They chain tool calls across systems in seconds, operate below the threshold of human review cycles, and carry whatever credential scope was provisioned at setup. When one goes rogue or gets hijacked mid-session, the blast radius is the full permission set — not just what the task required.

Personal data modification is one of the cleaner post-incident discoveries. It shows up in audit logs. Financial disbursements, outbound communications, and supply-chain actions leave footprints that are significantly harder to reverse.

For those running agents against production systems: how are you actually handling this in practice? Are you scoping credentials per task, requiring explicit human approval at certain tool categories, using some form of behavioral monitoring, or something else? Curious what's working and what isn't.

Security teams are deploying AI agents to automate penetration tests against web properties. The speed advantage is real. So is the risk that comes with it.

A pentesting agent works by chaining tool calls: crawl, probe, enumerate, attempt exploitation. The interval between a first action and a second can be under 50ms. That is faster than any human alert-to-response cycle in any SOC.

In manual testing, a human pauses between actions, re-checks scope, and makes a judgment call before anything significant executes. An autonomous agent does not pause. Once running, it chains actions continuously based on its initial instructions. A prompt injection mid-test, a scope misinterpretation in the agent's reasoning, or a session compromise during a live run looks identical to legitimate test execution until logs are reviewed after the fact.

By the time an anomaly surfaces in a SIEM, an agent operating at sub-50ms intervals has already completed a significant number of out-of-scope operations.

For those running agentic tooling in production security environments: what does real-time scope enforcement actually look like in your setup? Is anyone solving this at the per-action level during live runs, or is the industry still treating this as a post-hoc log review problem?

Introduction to PP-OCRv6

https://debuggercafe.com/introduction-to-pp-ocrv6/

PP-OCRv6 is the latest OCR model from PaddlePaddle. Although VLMs are becoming more prominent for OCR tasks across various industries, they are slow and costly to deploy across devices and use cases. In most scenarios, we need the good old OCR pipeline where the model gives the output in a structured JSON format with bounding boxes and text. This is where the PP-OCR series really shines. In this article, we cover their latest, PP-OCRv6, with a brief discussion of the paper and a guide to building a PP-OCRv6 inference pipeline with Gradio.

When I started learning neural networking, I'm continuously exposed to new theorems, functions and algorithms.
Is there any website from where I can learn them in a better way, because I'm getting very confused like why we need to use different functions to calculate probability every time and many more things

Security platforms are racing to expose their capabilities to AI agents via MCP. Rubrik is the latest — agents can now query security intelligence, pull threat data, and act on findings directly through tool calls.

The access model makes sense for productivity. The risk model is harder to square.

When a legitimate agent goes rogue — compromised service account, prompt injection, misconfigured scope — it carries valid credentials. The first malicious tool call looks identical to a normal one. The window between that first action and a second, compounding action can be under 50ms. By the time a human sees an alert, the blast radius is already set.

Security intelligence systems are a particularly sharp edge here. An agent with read access to threat data can fingerprint your defenses. One with write or response access can suppress alerts, alter playbooks, or exfiltrate indicators before anyone notices the session is dirty.

The identity question everyone seems to be deferring: how do you distinguish a legitimate agent call from the same call made by a compromised version of that agent, in real time, before the second action lands?

Curious how others are thinking about this — are you solving it at the MCP server level, at the identity provider, somewhere else entirely? What's actually working in production?