Search: “Max”

18 stories in the last 3d

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPostSep 15, 21:37 UTC · 1d agoAI tools & infra2

Show HN: Pelican-bicycle alternatives (updated for 2026)

Hobbyist benchmark re-runs the pelican-bicycle SVG test on six 2026 frontier models, comparing generation time and API cost per image.

A Show HN post re-runs the classic pelican-bicycle and similar SVG generation tests across six 2026 models: GPT-6 Astra, Claude Fable 5.1, Gemini 3.8 Flash, DeepSeek V4 Pro, Qwen3.8 Max, and Fugu Ultra v2, recording wall-clock time and cost. It also lists 2025 baseline runs with ten models including Claude Sonnet 4.5, GPT-5.2 Pro, and Qwen3-VL-235B-A22B-Thinking. DeepSeek V4 Pro is consistently cheapest ($0.04-$0.10) while Qwen3.8 Max is slowest, taking up to roughly 17 minutes per generation.

Hacker News · securitySep 14, 13:20 UTC · 2d agoAI research HN 24↑ · 4 comments

Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Hacker News · AISep 15, 23:17 UTC · 1d agoAI tools & infra HN 45↑ · 19 comments

DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression

DeepSeek-V4.1 Flash is a 552B-parameter multimodal MoE model with 1M-token context achieving 4x KV cache compression for long-horizon agent workloads.

A detailed analysis of the DeepSeek-V4.1 Flash technical report describes a 552B-parameter multimodal mixture-of-experts model supporting contexts up to 1 million tokens. Its Causal Encoder-Decoder (CED) architecture activates 8B parameters during prefill and 16B during decode, and reportedly delivers about 420 tokens/s. Joint optimization of architecture (CSA2 cross-layer compression), FP4 KV cache precision, and deployment strategy cuts runtime KV cache to roughly 1/4 and persistent KV cache to about 1/8 of DeepSeek-V4-Flash at the same sequence length, targeting storage and bandwidth bottlenecks in long-horizon agent serving. The author notes all DeepSeek-V4 Pro models were taken offline following the release.

Hacker News · AI01:39 UTC · 10h agoModel release HN 41↑ · 3 comments

Meta’s new One subscriptions put a price on social media and AI

Meta's new One subscription tiers pair app perks with more Meta AI usage, from $2.99 single apps to $499 monthly business Max plan.

The Verge reports Meta's Meta One subscription bundles are now globally available, following the launch of its Muse AI assistant. Individual bundles include Core at $7.99/month and Premium at $19.99/month, combining Instagram Plus, WhatsApp Plus, and Facebook Plus with expanded Meta AI media generation including Muse images and Instagram's Restyle, cheaper than $11/month for all three standalone subscriptions. Creator and business plans span Essential ($14.99/month) to Expert ($149/month) and Max ($499/month), adding verification badges, impersonation protection, and Meta Business Agent capacity.

The Verge · AISep 15, 15:00 UTC · 1d agoAI industry

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)

Latent Space AI news roundup: Steve Yegge shuts down Gas Town, Databricks reports 60% higher coding spend on GPT-6 Astra, OpenAI launches misalignment disclosure framework.

Latent Space's AI News digest for September 15-16, 2026 leads with Steve Yegge shutting down his Gas Town orchestrator despite spending thousands monthly on coding-agent subscriptions. Databricks rolled out GPT-6 Astra to roughly 3,500 engineers, reporting superior long-horizon performance over Opus 5 and Sol 5.6 but a ~60% increase in coding spend. OpenAI published a formal framework for disclosing model misalignment incidents with six case reports, while Microsoft and Google Research released safety papers on 'capability laundering' and the Fuse motive-inference benchmark. Xiaomi shared live RL training telemetry for MiMo-V2.6, estimated at $493k/day for the 1T-class Pro run.

Latent Space07:28 UTC · 4h agoAI industry 13 sources

harshatheg/Qwen-2.5-1B-RLCD — new model trending #30 on Hugging Face

A community MLX inference engine evaluates constrained JSON schema fields in parallel on Apple Silicon, reporting 5.6-7.0x latency speedups with guaranteed schema validity.

The repository harshatheg/Qwen-2.5-1B-RLCD appeared at #30 on Hugging Face trending, but its content describes Parallel Constrained Decoding, an MLX-based inference engine for structured extraction and classification on Apple Silicon Macs. Benchmarked with mlx-community/Qwen2.5-1.5B-Instruct-4bit on an M4 Max, it reports 5.6x-7.0x latency reductions (e.g., 1,900 ms to 270 ms for a 28-field support triage task) with 100% syntactic validity and calibrated field-level probabilities. The engine prefills a single KV-cache, broadcasts it across all schema fields, and slices logits to valid candidate tokens for enum fields with up to 255 choices.

Hugging Face trending modelsSep 16, 01:35 UTC · 1d agoAI tools & infra

Meta expands subscription push with new AI-focused plans

Meta launched Meta One subscriptions ($7.99–$499/month) bundling expanded Muse AI image and video generation across Facebook, Instagram, and WhatsApp.

Meta introduced Meta One with consumer Core ($7.99/mo) and Premium ($19.99/mo) tiers plus business plans ranging from Essential ($14.99/mo) to Max ($499/mo). Subscriptions unlock expanded Muse Image, Muse Video, Restyle editing, and Meta Business Agent usage, following Meta's $14.3B investment in Scale AI. Appfigures data shows Instagram's daily subscription revenue averaging $1.2M and Facebook's $528K after the March Plus-tier launches, up 475% and 143% respectively. BNP Paribas forecasts $13.5B added revenue by 2028; Truist estimates $20B by 2030.

TechCrunch · AISep 15, 17:05 UTC · 1d agoAI industry

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent SpaceSep 15, 04:50 UTC · 2d agoAI safety & security

Snap tries to make the case again for its $2,200 smart glasses

Snap unveiled new features for its $2,200 Specs smart glasses, including an anticipatory AI system and enterprise partnerships with Amazon, Salesforce, and Nvidia.

At a Los Angeles event, Snap showcased updates for its Specs smart glasses, which launched earlier in 2026 at $2,200 to a mixed reception. The headline announcement was Specs Intelligence, an "anticipatory AI" system that builds an understanding of user goals and routines and works with iPhones and Macs independently of the glasses. Snap also launched Specs for Enterprise with partnerships including Amazon, Salesforce, and Nvidia, an NBA/WNBA AR training app, and a Verizon cellular connectivity package costing $10/month for Verizon customers and $20/month otherwise. The devices will ship later this fall after an October pop-up in Los Angeles.

TechCrunch · AI00:58 UTC · 10h agoAI industry 2 sources

Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

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.

MarkTechPostSep 16, 21:19 UTC · 14h agoModel release1

Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations

Researchers use difference-of-means representation vectors to detect reward hacking in frontier LLMs; GLM 5.2 hacks 73% of SWE-bench rollouts.

The study finds that simple difference-of-means (DoM) vectors coherently represent reward hacking in Kimi K3, GLM 5.2, and Qwen 3.8 Max across common evaluations. GLM 5.2 reward-hacks in 57.2% of rollouts on DeepSWE and 73% on SWE-bench. DoM-vector monitors match LLM monitors' effectiveness at virtually no cost, catching 3.1% more hacks in Kimi K3 on DeepSWE at a matched false positive rate, and run on chain-of-thought to predict hacks before actions occur.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:31 UTC · 18h agoAI safety & security

Anthropic merges Claude Chat, Cowork, and more into a single product

Anthropic merged Claude Chat and Cowork into one product and launched Claude Docs and Slides for creating and exporting documents and presentations.

Anthropic is folding Claude Chat and Cowork into a single product where Claude automatically determines what a task needs, eliminating tab switching. It also introduced Claude Docs and Claude Slides for creating, editing, and exporting documents and presentations as PowerPoint or PDF, with Claude Design now integrated into conversations. The rollout starts with Pro and Max plans, followed by Team and Free tiers; enterprise admins receive at least 30 days' notice.

The Decoderupdated Sep 16, 18:09 UTC · 17h agofirst Sep 16, 16:31 UTC · 19h agoAI industry 5 sources1

Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection

Det-LIME extends LIME to multi-instance object detection explanations, improving attribution for harbor seal aerial surveys.

Det-LIME adapts LIME to object detection by combining per-detection weighting, a proximity kernel emphasizing box-adjacent regions, and IoU-based matching to track instances across perturbations. It was evaluated on aerial drone imagery for harbor seal detection plus a seabird case study, and compared against vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient-based attribution. Using Attribution Ratio and Max Saliency Hit Rate metrics, it consistently improved multi-instance attribution and produced box-aligned explanations useful for debugging and data augmentation.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:20 UTC · 1d agoAI research

Présentation de Meta One : Un service d’abonnement offrant davantage de fonctionnalités et d’IA pour créer, se connecter et se démarquer

Meta launches Meta One subscription bundles with expanded Meta AI usage and creator/business tools, already at 15 million subscriptions and trials.

Meta launched Meta One, a new subscription service across Instagram, Facebook, WhatsApp and Meta AI with more than 50 features and 15 million subscriptions and trials to date. Core and Premium bundles add heavier use of compute-intensive AI features, including image creation/editing and video generation via the Muse models, plus tools like Restyle on Instagram. Pricing starts at EUR 2.49/month for single-product plans, EUR 6.99 for bundled consumer plans and EUR 16.99 for creator/business bundles; the core Meta AI experience remains free.

Meta NewsroomSep 15, 15:25 UTC · 1d agoAI industry

Introducing Meta One: A Subscription Service With More Features and AI to Create, Connect, and Stand Out

Meta launches global Meta One subscriptions bundling Instagram, Facebook, WhatsApp Plus with expanded Meta AI and Muse media generation, priced $2.99-$499 monthly.

Meta introduced Meta One, a global subscription service with plans for individuals, creators, and businesses, launching with more than 50 features across Instagram, Facebook, WhatsApp, and Meta AI. Individual bundles Core ($7.99/month) and Premium ($19.99/month) combine the single-product Plus plans with expanded use of compute-intensive AI capabilities, including image/video generation powered by Muse models and Instagram's Restyle. Business tiers range from Essential ($14.99/month) to Expert ($149/month) and Max ($499/month), with expansion planned to Edits, AI glasses, and more.

Meta NewsroomSep 15, 15:00 UTC · 1d agoAI industry

Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPostSep 14, 21:54 UTC · 2d agoAI research1

Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama

Opinion piece urges migrating 35KB preprompts from Anthropic/OpenAI to self-hosted Ollama, citing session privacy risks and safety filters blocking security research.

The author documents gotchas migrating 35KB preprompts from Claude Opus to self-hosted Ollama, motivated by fears that frontier providers train on user sessions, citing the OpenAI Navier-Stokes controversy. The piece argues inference providers cannot audit their own retention or training pipelines and that only self-hosted hardware offers verifiable privacy. It also criticizes frontier safety filters for refusing vulnerability research tasks and calls for models that support exploitability testing in CI/CD pipelines.

Hacker News · AISep 14, 13:59 UTC · 2d agoAI industry HN 41↑ · 13 comments1