Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Simon Willison built a GeoJSON map viewer with PNG export using GPT-5.6-Sol, Claude Code for web, and Fable 5.1.
Simon Willison describes building a GeoJSON map viewer that displays boundary files and exports maps as PNGs. GPT-5.6-Sol proactively offered to create the tool, and iterations used Claude Code for web and Fable 5.1. He also notes ChatGPT Work will assemble boundary polygons from government data sources when asked.
Simon Willison used ChatGPT Work with GPT-6 Astra to generate 5K/10K running loops from OpenStreetMap data, noting the agent's executed code was not visible.
Simon Willison prompted ChatGPT Work running GPT-6 Astra (Max) to design looping 5K and 10K running routes from his address using OpenStreetMap data. The agent ran for 27 minutes and delivered an embedded visualization plus downloadable GPX and GeoJSON files, stating it used Nominatim for geocoding and Overpass to fetch local OSM roads and trails. He notes frustration that the actual executed code and exact actions were not visible in the ChatGPT UI, highlighting a transparency gap in agentic AI products.
AllenAI's OlmoEarth Studio adds custom embedding exports to support downstream geospatial analysis workflows.
A Hugging Face blog post from AllenAI introduces OlmoEarth embeddings, a feature allowing custom embedding exports from OlmoEarth Studio for downstream analysis tasks. Only the title was available, so no benchmark or performance details are provided. OlmoEarth is Ai2's open geospatial AI model family.
Critical XSS CVE-2026-85061 in MapLibre GL JS enables zero-click attacks on an estimated 2.7 million users; fixed in maplibre-gl 6.4.1.
A flaw in MapLibre GL JS's DOM.sanitize() iterates a live NamedNodeMap while removing attributes, skipping malicious attributes placed adjacent to removed ones, letting event handlers like onload and ontoggle survive and execute via innerHTML in the attribution control. Tracked as CVE-2026-85061 and GHSA-jrc7-96c5-q579, the flaw affects maplibre-gl versions 6.4.0 and earlier, is rated critical under CVSS v3.1 (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:N), and requires no user interaction, privileges, or authentication. Exploitation could enable session theft, unauthorized actions, phishing redirects, or map content manipulation; the patch in 6.4.1 snapshots attributes with Array.from before iterating.
Unpatched GeoServer zero-day enabling SQL injection and possible RCE is being actively probed across exposed systems.
A zero-day in GeoServer's jsonArrayContains functionality allows unauthorized SQL injection and, in some configurations, remote code execution. Disclosed on August 12, 2026 without a CVE ID, it drew hundreds of exploitation probes from watchTowr within hours. No patch is available yet; GeoServer was previously mass-exploited via CVE-2024-36401.
MapLibre GL JS 6.4.1 patches CVE-2026-85061, a critical zero-click XSS in DOM.sanitize() affecting versions 6.4.0 and earlier.
A critical cross-site scripting vulnerability, CVE-2026-85061 (GHSA-jrc7-96c5-q579), affects maplibre-gl versions 6.4.0 and earlier and is fixed in 6.4.1. The flaw stems from DOM.sanitize() iterating a live NamedNodeMap while deleting attributes, causing the sanitizer to skip adjacent dangerous event-handler attributes that later execute via innerHTML. GitHub rates the issue Critical with CVSS v3.1 vector AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:N, exploitable without user interaction when untrusted attribution strings are rendered. Organizations should upgrade to 6.4.1 or later, rebuild affected bundles, and sanitize attribution fields as an interim measure.
Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.
HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.
Simon Willison used GPT-6 Astra (medium) in ChatGPT Work to build an animated D3 transition between Mercator and Equal Earth map projections.
Willison built an animated transition between the Mercator and Equal Earth map projections using D3. The tool was generated by GPT-6 Astra (medium) in ChatGPT Work. Equal Earth is a projection recently voted on at the UN. The post is a vibe-coding demonstration rather than a security or major model event.
ZDI disclosed a server-side request forgery in Fabric.js loadFromJSON (CVE-2026-19504) that can leak sensitive information from affected implementations.
Zero Day Initiative advisory ZDI-26-588 describes a server-side request forgery vulnerability in the Fabric.js canvas library's loadFromJSON function. Exploitation requires interaction with the library and attack vectors vary by implementation, potentially exposing sensitive information. The issue is rated 4.0 on the CVSS scale and tracked as CVE-2026-19504. The advisory does not report exploitation in the wild.
Next.js 16.4.0-canary.13 Image Optimizer retains a DNS rebinding TOCTOU SSRF in fetchExternalImage() allowing requests to private network resources.
Next.js 16.4.0-canary.13 contains a DNS rebinding time-of-check-to-time-of-use Server-Side Request Forgery vulnerability in the Image Optimizer's fetchExternalImage() function. The code resolves the supplied hostname and checks the resulting addresses with isPrivateIp(), but a TOCTOU gap between resolution and the actual fetch allows an attacker-controlled DNS record to change and reach private network resources.
Grouped Value Attention stores grouped values and reconstructs content keys via a learned linear map, cutting KV-cache size about 45-47% versus GQA.
GVA stores only grouped values and reconstructs content keys with a learned linear map absorbed into the query at decode time, while a small shared decoupled RoPE channel preserves positional information via a separately cached positional key. At 350M parameters trained on 30B FineWeb-Edu tokens, the 16-dimensional positional variant scores 44.18 average accuracy across five tasks versus 44.36 for GQA and 43.88 for MLA. Custom decoding kernels are in development with an open-source release planned.
Knowledgator released GLiFormer, an Apache-2.0 encoder (264M/575M) handling NER, classification, relations, and nested JSON extraction, scoring 91.10 F1.
Knowledgator Engineering released GLiFormer, a schema-conditioned encoder that performs NER, classification, relation extraction, nested JSON structuring, and embeddings without generating output tokens. GLiFormer Large v1 has 575.6M parameters and scores 91.10 F1 on nested JSON extraction, close to GPT-5.6-luna's 91.96; both checkpoints are Apache 2.0 on Hugging Face. Reported median latency is 69 ms on GPU for the base model, though relation extraction (21.33 micro-F1) still trails GLiNER-Relex and larger LLMs.
Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.
Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.
Hacker News post showcases an interactive earth exploration website that GPT-6 generated from only five prompts.
The item is a Hacker News submission (23 points, 32 comments) linking to earth.ethanplus.ai, an earth exploration website the author says was built by GPT-6 in five prompts. It functions as a community demo of the model's web development and generative capabilities rather than a research or security disclosure.
Version 0.34 of Simon Willison's llm CLI adds response-duration metrics to log output, plus bug fixes and faster log querying.
The open-source llm command-line tool for interacting with large language models released version 0.34. The headline change adds response duration in milliseconds and human-readable form to llm logs --usage Markdown output, plus a new duration_ms field in llm logs --short. The release includes several contributed bug fixes and a significant performance improvement to llm logs, alongside the related llm-openrouter 0.7.1 release.
A developer released gpu-lexer, a 27.5KB language-agnostic syntax highlighter that uses a tiny WebGPU model to label code tokens in the browser.
gpu-lexer splits source into words, whitespace, and symbols, then a small WebGPU model uses local and whole-file context to assign nine token classes, working on languages never seen in training. On held-out files, 12.57% of token labels differ from Shiki, though this measures agreement with Shiki rather than objective correctness. In benchmarks against Shiki 4.4.3, Prism.js, Highlight.js, Sugar High, and Starry Night, it highlighted 10 concatenated copies of three.min.js (5.56M characters) about 10x faster on an Apple M4 Pro in Chrome 152. The author frames it as an experiment, not a grammar-equivalent highlighter.
RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.
RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.
GeoNetwork patched two chained flaws (CVE-2026-63219, CVE-2026-58400) enabling unauthenticated RCE in government geospatial metadata catalogs.
Two flaws in the open-source GeoNetwork catalog chain into unauthenticated RCE: a missing authorization check on the formatter upload endpoint (CVE-2026-63219, CVSS 8.6) and an unsafe Saxon XSLT processor configuration allowing Java execution (CVE-2026-58400, CVSS 9.1). Fixes shipped in versions 4.4.12 and 4.2.17 on July 8, 2026, with details published August 31. Ethiack fingerprinted 121 internet-exposed vulnerable deployments across 39 countries, 89% tied to government or military agencies. No exploitation in the wild or KEV listing was reported at disclosure.
Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.
The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.
GLIE compresses visual document retrieval embeddings to four vectors per page while retaining nearly 80% of uncompressed nDCG@5 accuracy.
Researchers analyzing late-interaction retrieval embeddings found they lie exactly on the unit sphere and concentrate near a manifold of intrinsic dimension five to six. GLIE learns a few k vectors per page that serve as a lightweight index and a basis to regenerate the full embedding set for exact rescoring of top candidates at query time. On ViDoRe v1 with four vectors per page, GLIE retains nearly 80% of uncompressed nDCG@5 versus 70% for the best prior post-hoc method, using a 415K-parameter network trained in under three GPU-minutes on 1,000 pages.
Jeroen Roovers links a header-less LLM-generated advisory to a similar fake llama.cpp GGUF parser advisory from May 2026.
In the 'AI slops from Eve' oss-security thread, Jeroen Roovers asks whether a current LLM-generated message without a Date header comes from the same source as a May 15, 2026 posting titled 'Security Advisory: Multiple Vulnerabilities in llama.cpp GGUF Format Parsers'. Both fake advisories share the missing Date header trait. The exchange highlights growing LLM-generated noise on security mailing lists.
A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.
A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.
Insecure deserialization in Kaltura's legacy HTML5 Player V2 (mwEmbed) allows arbitrary file read and remote code execution; Player V7 is unaffected.
CERT/CC issued VU#308749 for two flaws in Kaltura's HTML5 Player V2 library (mwEmbed/html5lib), both caused by the same insecure deserialization bug. The flaws enable arbitrary file read and remote code execution via the exposed mwEmbedLoader.php endpoint. Affected versions include html5lib v2.45, v2.103 and earlier, and other v2.x releases; the currently supported Player V7 is unaffected. No evidence of exploitation was cited.
Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.
Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.
Atlas delivers zero-knowledge proofs for HNSW semantic search, verifying RAG retrieval in under a second on SIFT1M and 2.0 seconds at 100M vectors.
Atlas lets a search provider prove that a query was answered correctly against a committed HNSW index without revealing the index, addressing provider deviations like truncation or bias. It combines offline preprocessing, a fixed-size-state restructuring of HNSW with a correctness proof, and timestep-tagged batching of per-step arguments. The system proves queries in under a second on SIFT1M and 2.0 seconds at 100 million vectors while preserving plaintext HNSW recall, and proven retrieval maintains end-to-end RAG answer quality at lower cost than prior verifiable retrieval systems.
Researchers introduce KnowChange, a framework that uses pretrained vision-language models to synthesize realistic change-detection training data for remote sensing.
KnowChange is a knowledge-guided change data synthesis framework that leverages pretrained vision-language models to reason about plausible change locations and class transitions from pre-change scenes and desired change types. It addresses the limited class-transition coverage and inflexibility of handcrafted rule-based synthesis methods, enabling diverse change types in a unified pipeline. Experiments show KnowChange-generated data outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite compact generation scale.
Google launched WeatherNext 3, an AI weather model using raw satellite data that beats ECMWF and now powers Search, Gemini, and Maps.
Google released WeatherNext 3, an AI weather forecasting model that incorporates physical surface information (land/ocean type and elevation) to improve surface temperature and dewpoint calculations, improving point location temperature accuracy by up to 30 percent. Its white paper reports roughly 5 percent better upper-atmosphere accuracy than WeatherNext 2, equating to about six additional hours of forecast lead time, outperforming the ECMWF AI model on these metrics. The model now supplies forecast information across Google Search, Gemini, and Maps, though the paper notes unexplained short-lead degraded results and grid-shaped artifacts in some predictions.
Canada's Cyber Centre (AV26-913) warns GeoVision GV-LPC2011/LPC2211 firmware 1.13 license-plate cameras are affected by vulnerabilities and urges updates.
The Canadian Centre for Cyber Security issued control-systems advisory AV26-913 on September 11, 2026, noting that GeoVision GV-LPC2011/LPC2211 firmware version 1.13 is affected by vulnerabilities. The bulletin references the vendor advisory GV-LPC-2026-09-01 but provides no CVE identifiers or exploitation details. Administrators are encouraged to review the linked advisories and apply updates as they become available.
XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.
The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.
Researchers release SpatialBlock-15k, a synthetic block-stacking dataset that improves 3D spatial reasoning in large vision-language models without dense geometric annotations.
The paper addresses limited spatial intelligence in LVLMs by training on structured block-manipulation tasks instead of costly real-scene annotated datasets. SpatialBlock-15k contains 15,000 synthetic problems covering 3D-to-2D projection, viewpoint transformation, and structural combination, with color modulation as visual cues. LVLMs trained on it via direct answering or reasoning-based prediction outperform baselines and generalize to real-world spatial tasks. Code and data are released on GitHub.