Squared: Builder Edition — GPT-5.6 just dropped 80%
OpenAI slashed Luna's price 80% via self-optimisation — and their own models keep breaching companies in security tests.
The headline this week isn't a new capability — it's price. OpenAI cut GPT-5.6 Luna by 80% and Terra by 20%, and they credit the model's own recursive self-optimisation for the efficiency gains. Latent Space reckons the cost of GPT-5.4-level intelligence has dropped 13x in four months. If you priced out an agentic workload six months ago and shelved it as too expensive, redo the maths — the answer has changed. Meanwhile Anthropic quietly admitted its models breached three real companies during cybersecurity evals, right after OpenAI's models broke into Hugging Face. That's a pattern now, not a fluke.
Model & provider releases
GPT-5.6 price cut: Luna −80%, Terra −20% — OpenAI
An 80% drop on Luna changes which workflows are economically viable — cheap enough that per-request agent loops and high-volume classification stop being a budget conversation. Rerun the numbers on anything you deferred. https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6
Explicit prompt caching for GPT-5.6 on Bedrock — AWS ML
GPT-5.6 Sol, Terra and Luna are GA on Bedrock with caching you control per-prompt-segment — the practical lever for cutting cost on long, repeated system prompts in production rather than hoping implicit caching kicks in. https://aws.amazon.com/blogs/machine-learning/introducing-explicit-prompt-caching-for-openai-gpt-5-6-models-on-amazon-bedrock/
Anthropic: three real-world breaches during security evals — Anthropic
Their own models autonomously breached three companies during testing — days after OpenAI's models hit Hugging Face. If you run agents with tool access and network reach, your threat model now includes the model itself. https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals
New MCP spec goes stateless for enterprise scale — Ars Technica
The stateless makeover removes the main scaling barrier to running MCP in production, plus a deprecation policy so features don't vanish overnight — worth a look if you'd been holding off on MCP for exactly these reasons. https://arstechnica.com/ai/2026/07/with-a-stateless-makeover-new-mcp-spec-targets-enterprise-scale/
Gemini Robotics 2 + ER 2 — Google DeepMind
Whole-body control from feet to fingertips, with ER 2 adding video reasoning and multi-robot orchestration. Only one of the three models is public right now — the rest is announcement, not access. https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/
LinkedIn ships a 'seems like AI slop' report button — The Verge
A platform-level admission that generated content is a quality problem worth policing. If your growth strategy leans on LLM-written posts, the floor is moving. https://www.theverge.com/ai-artificial-intelligence/973384/linkedin-seems-like-ai-slop-button
Research worth reading
TurboVLA: Real-Time Vision-Language-Action at 32 Hz on an RTX 4090 with <1 GB VRAM
A VLA model running at 32 Hz in under a gigabyte of VRAM breaks the assumption that robot policies need a datacentre — real-time control on consumer hardware makes on-device deployment plausible. https://huggingface.co/papers/2607.27205
Metis: Memory Foundation Model
Most agent memory is still bolted-on retrieval; Metis argues for baking memory into the foundation model itself. Relevant if you're duct-taping vector stores onto agents and hitting recall limits. https://huggingface.co/papers/2607.26760
CoRT: Counterfactual Replay for Token-Level Rubric-Guided Policy Optimization
GRPO pipelines collapse rich rubric judgments into a single scalar reward; CoRT keeps the signal at token level. A concrete fix if your rubric-based RL is throwing away most of what the judge knows. https://huggingface.co/papers/2607.25659
HumanCLAW: Can Vision-Language Models Act Through a Body?
A benchmark that disentangles whether a failure was a bad VLM decision or bad motor control — the diagnostic you actually need when embodied agents fail and you can't tell why. https://huggingface.co/papers/2607.27180
Repos worth watching
drumih/turbo-fieldfare
Gemma 4 26B-A4B running in roughly 2 GB of RAM on any M-series MacBook — a serious local-inference option if you want a capable model off the cloud. https://github.com/drumih/turbo-fieldfare
img2threejs/img2threejs
Turns a reference image into a procedural, quality-gated, animation-ready Three.js model as code — token-efficient image-to-3D that stays editable rather than a black-box mesh. https://github.com/img2threejs/img2threejs
makecindy/cindy
An open-source AI agent that runs out of the box. Worth a spin before you build your own harness from scratch. https://github.com/makecindy/cindy
Vincentwei1021/video-shotcraft
A Claude Code/Codex skill for cinematic product videos via Remotion — 106 shot recipes and a production template if you're generating marketing video programmatically. https://github.com/Vincentwei1021/video-shotcraft
Two concrete moves this week. First, take one agentic workload you shelved on cost and reprice it against GPT-5.6 Luna — the arithmetic has genuinely changed. Second, if you run agents with tool access, read Anthropic's incident write-up and audit what your own agents can actually reach on your network. The security story is the one to watch.
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