Nano-Banana Pro makes turning complex Mermaid diagrams into clear images a breeze. Its ability to handle intricate syntax and quickly produce visuals is impressive. This tool is a real asset for anyone who works with diagrams regularly. 💡

The latest Grok Imagine update is truly impressive. It's fascinating to see how quickly this tool is evolving and pushing the boundaries of what we thought was possible. Every new release seems to bring unexpected features and improvements, making it an exciting time to follow its progress. 🚀

Claude Mythos Preview is now in private testing at $25/$125 per million tokens, but access is strictly limited to approved partners.

Because of its advanced cybersecurity abilities, Mythos isn’t available to the public. Instead, a select group—including major tech companies—are testing it through a $100M+ initiative called Project Glasswing.

What sets Mythos apart? It goes beyond typical bug-detection—this model autonomously chains multiple vulnerabilities to reach root or remote code execution, bypassing defenses that usually require rare human skills. During internal tests, it uncovered zero-days across all major systems, patched a 27-year-old OpenBSD flaw, and exploited old FreeBSD and Linux bugs—all on hardened systems. Even staff without security experience could request powerful exploits.

Anthropic just submitted patches to FFmpeg, reportedly using its latest model, Claude Mythos. The code quality is indistinguishable from that of top-tier engineers.

If these were truly discovered and written by AI, things are about to get really strange, very fast.

OpenAI is preparing to introduce a new model with enhanced cybersecurity features, but like Anthropic’s Mythos Preview, it will only be available to a select group of trusted organizations for now. This cautious, phased release reflects a growing trend: as AI models get better at independent hacking tasks, companies are starting to handle launches much like responsible vulnerability disclosures, emphasizing control and oversight over speed. 🔒

Avatar V just achieved a Face Similarity score of 0.840 and a Sync-C score of 8.97, leading its category. For reference, the highest possible “ground truth” is measured at 0.861. With just one more update, HeyGen’s avatars could become almost impossible to distinguish from real video.

We’re now introducing Avatar V, which finally solves the challenge of character consistency. Just record yourself for 15 seconds and, from that moment on, the system can generate videos in any style while perfectly capturing your likeness, expressions, and quirks.

This model is the new standard for AI avatars—a bold statement, but the data backs it up. 👀

⚖️🤖 Musk sues OpenAI for "stealing a charity." Jury: you waited too long.

The AP reports that a federal jury in Oakland unanimously tossed Elon Musk's lawsuit against Sam Altman and OpenAI. The jury found Musk had three years to sue and simply did not file on time. Judge Gonzalez Rogers immediately adopted the verdict and dismissed all claims.

90 minutes of deliberation. Three weeks of trial, private emails, Brockman's diaries, Satya Nadella on the stand. Didn't matter. The merits never got touched.

By ruling on timing, the jury dodged the actual question of whether donors to nonprofit AI labs have standing to challenge for-profit conversions under charitable trust law. That question is still live. Musk's team says they're appealing.

OpenAI heads toward its trillion-dollar IPO with the biggest legal threat dead on arrival, and accountability for the nonprofit-to-profit pivot remains untested.

🇨🇳⚡️ Ex-Samsung chip chief says China will pop the DDR5 price bubble by H2 2027

Kye-hyun Kyung, former head of Samsung's DS Group, says Chinese firms investing aggressively in memory production could flood supply and push prices down in the second half of next year.

CXMT leads the charge. Kyung cites data projecting global production capacity at 6 million wafers/month by H2 2027.

He warns that if Big Tech cuts AI budgets, the industry could flip straight into severe oversupply.

Source

⚡️ AMD's local AI box hits $3,999. It was $1,700 last summer.

AMD's Ryzen AI Halo workstation opens pre-orders next month at $3,999 with 128GB LPDDR5X. Same chip, same memory, same story. Memory prices exploding since launch are now fully baked into the consumer price.

For $4k you're one click away from an NVIDIA DGX Spark. Tough sell.

Source

⚡️ Bigger quant beats aggressive compression on Qwen3.6 35B

ByteShape benchmarked NTP vs MTP GGUFs across 4090, 5090, 4080, 5060 Ti, plus Intel, Ryzen, and Raspberry Pi 5.

MTP gives GPUs a 20-40% gen-speed boost. CPUs? It just adds overhead. Stick to NTP on CPU.

Lower bpw is not automatically better. Largest quant that fits often won.

🔐 1Password keeps your secrets out of Codex's brain entirely

1Password released an MCP server for OpenAI Codex that pulls credentials from vaults at runtime, then discards them. Mounted, used, gone.

Credentials never appear in code, terminals, or the model's context window. That last part matters most.

Just-in-time credentials are the new seatbelt for AI coding agents.

⚡️ Beijing's Booster Robotics is quietly becoming the platform that runs robot soccer

Each team runs fully autonomously, driven by AI, with zero human intervention. The slapstick hides real tech.

Booster's T1 robots trot, tackle, shoot, and defend entirely on their own. Teams bolt their own AI models on top of Booster's hardware, making it the Android of humanoid robotics.

China isn't just building robots. It's building the platform everyone else runs on.

🤖 The "Cannes AI film" didn't actually screen at Cannes

Higgsfield AI's Hell Grind, a 95-minute generative sci-fi feature built by 15 people in 14 days for under $500k, was pitched as a Cannes debut. The festival itself went out of its way to say otherwise.

Higgsfield held screenings at a private industry event and a commercial cinema in the town of Cannes, not a Festival de Cannes venue. The CEO still called it a Cannes premiere on LinkedIn. So.

80% of that $500k went straight into AI compute costs, which is the one genuinely interesting data point here.

🤖 Canada wants its own supercomputer. No AWS required.

Canada's "AI for All" strategy calls for a public AI supercomputer and investment in sovereign, Canadian-owned compute and cloud infrastructure. That's the government treating compute as national infrastructure, not a vendor contract.

Canada's current AI data centre and cloud setup is "largely foreign-owned," and sovereign compute capacity is "nascent" with "significant investment" needed to cut reliance on foreign providers. So yeah, the stakes are real.

Carney launched the plan Thursday. Big ambitions, thin details so far.

⚡️ Anthropic's Mythos hits 52x on training code. Humans top out at 4x.

Anthropic runs the same test every release, optimizing training code. A skilled human working 4-8 hours gets 4x. Claude Opus 4 averaged 3x in May 2025. Mythos Preview hit 52x by April 2026.

Anthropic frames it as a possible path to recursive self-improvement, but admits Claude still hasn't shown the research taste to pick which problems matter.

Narrow task, lab conditions, unreleased model. Still a brutal trendline.

⚡️ Google is renting $920M/month of compute from SpaceX

110,000 Nvidia GPUs, CPUs, memory, the whole stack, Oct 2026 through June 2029.

Google is already the world's largest single owner of AI compute, but demand still caught them short. Anthropic just signed a similar deal at $1.25B/month for the Colossus 1 cluster.

xAI built those data centers to run Grok. Now it's renting them to its biggest rivals. Honestly, not a bad pivot.

🚨🔥 GOP calls the anti-data-center movement a Chinese psy-op. The FBI might investigate.

Republican lawmakers are demanding the FBI probe whether rising anti-AI sentiment is a foreign influence op run by China. But the reports they're citing don't establish direct coordination. They point to funding relationships and "overlapping messaging."

One AEI fellow put it bluntly: "Pretending AI anxiety is fake... is the surest path to failure."

Real concerns (energy bills, water, noise) don't need Beijing to exist.

⚡️ Local AI agent built from scratch, not from vibes

OpenLumara is a hand-written local agent framework: tiny system prompt, extreme token efficiency, everything modular and optional. Basically the opposite of every bloated cloud agent SDK.

People running it have found that fewer tools means better reasoning. Give a small model 2-3 focused tools and it stays sharp. Dump the whole toolbox on it and you get context rot.

⚡️ Unitree G1 hauls a load up stairs. Humanoid hardware is quietly lapping the software.

The G1 carries over 40kg while walking and can climb stairs up to 40cm high. It's already the best-selling humanoid on the market, with 1,000+ units shipped.

The robots are ready. The AGI to pilot them isn't. Hardware won't be the bottleneck.

🧠 Asimov saw the cognitive offloading problem coming. In 1956.

The calculator panic of the 1980s is back, just wearing an LLM costume. The fear is the same, that kids outsource thinking to machines and lose the baseline skills that make harder thinking possible.

But LLMs are scarier. A calculator can't write your essay, reason through your argument, or code your app. The surface area of offloadable cognition is basically everything now.

Asimov's Multivac stories kept asking what humans do when machines know more. Turns out the answer wasn't "thrive." It was "forget how to think."

⚡️ 120 tok/s on a 12GB GPU. Gemma 4 12B just got silly fast.

Pair the new QAT checkpoint with MTP speculative decoding in llama.cpp and you're hitting 120 tokens/s on a single consumer card. QAT minimizes quality loss by simulating quantization during training, so you're not trading brains for speed.

The dedicated MTP draft model enables significantly faster inference with no quality loss. The PR landed in llama.cpp mainline the same day it was shared.

Beats Qwen 35B MoE on throughput, fits in 12GB. That's a lot of model for a gaming GPU.

Here's step-by-step instructions to get this working:

1. Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp

2. Fetch and switch to the Gemma 4 MTP PR branch
git fetch origin pull/23398/head:gemma4-mtp
git checkout gemma4-mtp

3. Build with CUDA support for NVIDIA GPUs
cmake -B build -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=OFF
cmake --build build --config Release -j$(nproc)

4. Download Unsloth's Gemma 4 12B QAT here: https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF

5. Download Google's Gemma 4 assistant / draft here https://huggingface.co/Janvitos/gemma-4-12B-it-qat-assistant-MTP-Q8_0-GGUF

6. Load the models with llama-server
llama-server \
  -m gemma-4-12B-it-qat-UD-Q4_K_XL.gguf \
  --model-draft gemma-4-12B-it-qat-assistant-MTP-Q8_0.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 4 \
  --parallel 1 \
  --ctx-size 131072 \
  --temp 1.0 \
  --top-p 0.95 \
  --top-k 64