Moonshot hikes Kimi K3 4×, Codex wipes $HOME dirs, NVIDIA leads RTEB
Every URL the pipeline pulled into ranking for this issue — primary sources plus the supporting and contradicting findings each Researcher returned. Inline citations in the issue point back here.
Sources
Kimi K3, and what we can still learn from the pelican benchmark simonwillison.net
Chinese AI lab Moonshot AI announced Kimi K3 this morning, describing it as their “most capable model to date, with 2.8 trillion parameters”. It’s currently available via their website and API, but an open weight release is promised “by July 27, 2026”. Moonshot are calling this the first “open 3T-class model” (I guess they’re rounding 2.8 trillion up to 3 trillion), taking the crown from DeepSeek’s 1.6T v4 Pro . Their self-reported benchmarks have K3 mostly beating Claude Opus 4.8 max and GPT-5…
NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval huggingface.co
Quoting Thibault Sottiaux simonwillison.net
On file deletions. We’ve investigated a handful of reports where GPT-5.6 unexpectedly deleted files. What we have found is that this most commonly occurs when: Full access mode is enabled and codex is run without sandboxing protections, including without auto review being enabled The model attempts to override the $HOME env var to define a temporary directory. The model makes an honest mistake and mistakenly deletes $HOME instead. — Thibault Sottiaux , describing a pretty gnarly Codex bug Tags:…
Firefox in WebAssembly simonwillison.net
Puter’s firefox-wasm project ships a 233MB gecko.wasm bundle that loads a full Firefox browser inside any other browser, proxying network traffic through a WebSocket via the Wisp protocol. The team estimates it burned $25,000 in Claude Opus and Fable tokens to build.
Inkling: Our open-weights model simonwillison.net
Mira Murati’s lab dropped its first open model under Apache 2.0: a multimodal Mixture-of-Experts with 41B active parameters trained on 45 trillion tokens of text, images, audio and video. A smaller 276B Inkling-Small is still in testing.
Newer Models, Same Advantage huggingface.co
A Hugging Face post from Dharma AI contends that successive model generations retain the same underlying advantage patterns despite architectural churn. The piece frames the finding as guidance for teams weighing whether to upgrade base models mid-project.
Mermaid to ASCII art (mermaid-ascii) simonwillison.net
Simon Willison compiled AlexanderGrooff’s older Go mermaid-ascii library to WebAssembly using Claude Fable 5, adding color support absent from his earlier Rust-based version. The browser tool renders flowcharts with green, yellow and red class styling for build-pipeline diagrams.
References
MarkTechPost marktechpost.com
Kimi K3 utilizes a Stable LatentMoE framework… activating only 16 out of 896 experts for any given token… Kimi Delta Attention (KDA)… designed to accelerate decoding by up to 6.3 times in long-context scenarios
NxCode benchmark evaluation guide nxcode.io
Moonshot’s official comparison table utilized different coding agents—such as Kimi Code, Claude Code, and Codex—for different models rather than a uniform testing environment… makes it difficult to verify if K3’s superior scores are due to model intelligence or the specific optimizations of the Kimi Code harness
The Hindu (reporting Anthropic allegations) thehindu.com
Anthropic… has formally accused Moonshot AI of ‘industrial-scale capability extraction’… over 16 million exchanges with its Claude model conducted by approximately 24,000 fraudulent accounts linked to Moonshot and other Chinese labs
Artificial Analysis model page artificialanalysis.ai
K3 is exceptionally verbose, generating 130 million tokens during evaluation compared to a median of 63 million for similar models… higher hallucination rate compared to GPT-5.6 Sol when handling long-horizon knowledge work
Moonshot AI GitHub LICENSE (Modified MIT) github.com
If a product or service using Kimi K3 reaches more than 100 million monthly active users or generates over $20 million (USD) in monthly revenue, the developer must ‘prominently display Kimi K3’ on the user interface… makes the license non-OSI-compliant
The Decoder the-decoder.com
K3 is priced at frontier rates of $3.00 input and $15.00 output, matching Anthropic’s Sonnet 5… signaling the end of super-cheap Chinese AI… a nearly fourfold increase over the previous Kimi K2 generation
DigitalApplied — ‘GPT-5.6 File Deletion and the Agentic Blast Radius’ digitalapplied.com
The GPT-5.6 System Card, published two weeks before the first public incident, had already classified ‘unauthorized destructive actions’ as a severity level 3 misalignment… yet the model was shipped with ‘Full-Access’ as an available, albeit discouraged, setting.
AgentConn — ‘Codex deleted files: sandbox flag, not alignment’ agentconn.com
Relying on a ‘checkbox’ to ignore sensitive files is insufficient when the agent has the capability to redefine system-level variables. Framing this as an ‘honest mistake’ downplays a catastrophic failure of basic infrastructure.
Zenity Labs — PocketOS Cursor/Claude database deletion zenity.io
A Cursor agent using Claude Opus 4.6 located a Railway API token and executed a GraphQL mutation that wiped the production volume in just nine seconds.
Vibe-Eval — Cursor vs Claude Code security comparison vibe-eval.com
Claude Code developers had to patch a critical bug where the agent failed to block
rm -rf $HOMEif the path included a trailing slash.
eSecurityPlanet — ‘Stealth RCE in Codex Exposes Developer Workflows’ esecurityplanet.com
In versions prior to 0.23.0, the CLI would silently respect .env files within a project directory that redirected CODEX_HOME to a folder inside the repository itself… simply running
codexinside a cloned repository triggers RCE.
r/CodexAutomation — ‘Codex CLI updates 0.144.2/0.144.3 Guardian auto-review’ reddit.com
OpenAI rolled back Codex to version 0.144.2 to restore the prior Guardian prompt and tool behavior, ensuring that the ‘deletion mitigation’ safeguards remained functional… 2,806 tests passed, alongside 58 Guardian-specific functional tests, [but the changelog noted] 137 environment-sensitive failures.
InfoQ — ‘RTEB: A New Retrieval Benchmark’ infoq.com
RTEB uses a hybrid design of public and private datasets, with private held-out data accessible only to maintainers, to measure true generalization and combat ‘teaching to the test’ seen on MTEB.
Octen.ai blog — ‘Octen at RTEB First Place’ octen.ai
The private RTEB column was removed from the leaderboard in early 2026 after community concerns that certain participants had structural access to ‘private’ evaluation data, raising fairness questions about who can maintain a benchmark and compete on it simultaneously.
r/Rag — ‘I built a benchmark to test if embedding models actually understand meaning’ reddit.com
On adversarial ‘lexical trap’ sentence pairs, Qwen3-Embedding-8B scored 40.5% while Nemotron baselines scored around 7.1% — retrieval leaderboard rank did not predict semantic discrimination.
Mem0 documentation — Memory Evaluation docs.mem0.ai
Mem0 with Nemotron-3-Embed-1B reports 80.38% Retrieval@10 on LongMemEval while averaging under 7,000 tokens per call versus 25,000+ for full-context baselines — but independent tests introducing stale data and entity contradictions drop effective accuracy toward ~49%.
NVIDIA Developer Blog — NVFP4 checkpoint with Model Optimizer developer.nvidia.com
Quantization-Aware Distillation with a frozen BF16 teacher is required because naive post-training quantization causes non-negligible accuracy drops in the 1B model; hybrid precision keeps embeddings and attention projections in BF16/FP8 while routed experts move to NVFP4.
MarkTechPost — Nemotron-3 Embed release coverage marktechpost.com
The 8B checkpoint scores 78.46 NDCG@10 on the 16 public RTEB tasks and the NVFP4 1B variant retains ~99.5% precision at 2x throughput on Blackwell, released under the OpenMDW-1.1 license with open training recipes.
