Committee

The US has spent billions building a “virtual wall” of surveillance towers along its southern border over the past 25 years, promising they will help detect and apprehend border crossers and save lives. But a groundbreaking investigation by MIT Technology Review has documented over a thousand people who moved through areas watched by these towers without being reached or apprehended, and who ultimately died there. Some were even under the watch of newly installed AI-powered towers designed to spot people automatically. Our findings reveal a humanitarian crisis more visible than previously known, and repeated failures of the virtual wall’s basic security promise. Join MIT Technology Review editors and reporters for a conversation examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands. REGISTER NOW Speakers: Mat Honan, editor in chief, James O’Donnell, senior AI reporter, and Eileen Guo, senior features and investigations reporter Related Stories The US spent billions on border surveillance. Why can’t it catch people before they die? How we made the first comprehensive map of deaths along the US border’s “virtual wall” 4 ways to address the failures we found along the US border’s “virtual wall”

Anthropic has released Claude Opus 5.5, the first model in its new Claude 5.5 family. The team states it performs at the level of Claude Fable 5.1 on most work. It also costs 40% less to run than Opus 5 on typical workloads at default settings. On Anthropic’s own benchmarks, it leads in agentic coding, computer use, and knowledge work. Is it deployable? Yes, as a managed API model. Anthropic has not released weights, so self-hosting is not an option. Developers can call claude-opus-5-5 on the Claude Platform, Amazon Web Services, Google Cloud, and Microsoft Azure. Zero data retention is available, as with previous Opus models. Benchmarks: Strong Lead, Not a Clean Sweep Opus 5.5 scores use adaptive thinking at max effort, with production safeguards enabled. Benchmark Opus 5.5 Fable 5.1 Opus 5 GPT-6 Astra Terminal-Bench 4.0 66.4% 55.8% 52.3% 57.9% FrontierCode v1.1 54.4% 50.3% 48.0% 53.3% CursorBench 4.0 57.8% 51.8% 46.6% n/r GDPval-AA v2.1 (Elo) 1846 1735 1708 1542 OSWorld 2.0 81.8% 80.7% 74.0% n/r Terminal-Bench-Science 0.1 58.7% 52.6% 29.0% 64.6% AutomationBench 40.0% 31.4% 26.9% 41.4% Terminal-Bench 4.0 is reported at xhigh effort for Opus 5.5. GPT-6 Astra still leads on Terminal-Bench-Science and AutomationBench. Zapier ran AutomationBench without fallback models, so safeguard interventions counted as failures. Anthropic also cautions that benchmark margins are becoming a less reliable guide. In its own use, the gap to Fable 5.1 is narrower than the scores suggest. The cost-adjusted results are more telling. At default (medium) effort, Opus 5.5 scores 54.6% on FrontierCode. That beats GPT-6 Astra’s top score of 53.3% at about a fifth of the cost per task. On CursorBench, medium effort scores 52.5%. That is 11 points above GPT-5.6 Sol’s best, at about a third of the cost. Pricing and Speed Opus 5.5 needs less compute to serve than Opus 5, and pricing reflects that. Per 1M tokens Opus 5.5 Opus 5 Input $4 $5 Output $20 $25 Cache reads $0.20 $0.50 Cache writes $5 $6.25 Cache reads make up most agentic and coding costs, and they drop 60%. Opus 5.5 also uses fewer tokens per task. Together, that nets out to the 40% cost reduction. Output generation is more than 30% faster than Opus 5. Fast mode in Claude Code and the Claude Platform offers up to 2.5x speed at $8 input and $40 output per million tokens. Anthropic is also raising five-hour usage limits on Pro, Max, Team, and seat-based Enterprise plans. Subscribers get a rate limit reset they can save and use later. What Early Testers Reported One tester completed a 680,000-line code migration in less than a day. Another audited and fixed a 200,000-line codebase in under 3 hours. Opus 5 took over 20 hours and 2.5x the tokens. In an internal C to Rust port of HAProxy, Opus 5.5 finished in 9.5 hours. Fable 5.1 took 12 hours, and Opus 5.5 cost 51% less. Deloitte says Opus 5.5 at lowest effort caught 72% of known review bugs. Opus 5 at high effort caught 56%. In a hard-to-source earnings report test, 16 of 18 Opus 5.5 reports cleared Anthropic’s quality bar. Fable 5.1 and Opus 5 never did. Writing style also changed. Opus 5.5 puts key information first, uses less jargon, and follows the writing rules you give it. Safety, Safeguards, and API Changes Opus 5.5 is Anthropic’s first release since CEO Dario Amodei called for pacing the frontier. External evaluators including METR and Frontier Design tested it before release. It posts the best score to date on Anthropic’s automated behavioral audit, which covers nearly 2,000 scenarios. In a new containment test, it tried to circumvent boundaries about 85% less often than Opus 5. Anthropic also notes the model often suspects it is being evaluated. Its biology and cyber capabilities are comparable to Claude Mythos 5.1. So Opus 5.5 ships with safeguards similar to Fable 5.1: Cybersecurity: Routine bug finding and fixing works. Most other cybersecurity tasks are re-routed to Opus 4.8. The Cyber Verification Program will expand to Opus 5.5. Biology: Vetted organizations can apply to the Life Sciences Verification Program. Distillation: Preserved thinking stops API users from editing prior context to extract reasoning. It applies to API accounts created on or after August 31, 2026. Two more changes affect integrations. Thinking can no longer be disabled. Outputs also carry watermarking for EU AI Act compliance. Full details are in the Opus 5.5 System Card. Interactive Explainer Key Takeaways Opus 5.5 matches Fable 5.1 on most work and beats both Opus 5 and Fable 5.1 on nearly every reported benchmark. API pricing drops to $4/$20 per 1M tokens, and cache reads fall 60% to $0.20. Anthropic puts typical workload savings at 40%, with output over 30% faster than Opus 5. Cyber and biology requests hit Fable 5.1-class safeguards, and thinking cannot be switched off. Closed weights: claude-opus-5-5 runs via Claude Platform, AWS, Google Cloud, and Azure. Check out the Technical details here. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Anthropic Releases Claude Opus 5.5: Fable 5.1-Level Performance at 40% Lower Running Cost Than Opus 5 appeared first on MarkTechPost.

Alibaba’s Qwen team has released Qwen-Image-2.1, a unified text-to-image generation and image editing model. Its visual generation component has 7B parameters across 32 single-stream DiT layers. One checkpoint covers text-to-image, multi-reference editing, local edits, and transparent RGBA output. Is it deployable? Yes, for research and evaluation. Day 0 support covers Diffusers, ComfyUI, vLLM-Omni, SGLang, and LightX2V. Commercial deployment needs a separate license from Qwen. From 20B to 7B The original Qwen-Image shipped in August 2025 as a 20B model under Apache 2.0. Editing lived in a separate Qwen-Image-Edit checkpoint. Qwen-Image-2.1 folds both jobs into one model at about a third of the size. Qwen team calls it the most balanced and cost-effective model in the Qwen-Image series. One important thing to note here for capacity planning: the 7B figure covers the diffusion transformer only. The pipeline also loads an 8B Qwen3-VL encoder. Architecture The GitHub Repo lists 4 components: Transformer: 32 layers, 7B parameters, single-stream design with block-causal attention. Text encoder: Qwen3-VL 8B, which encodes text instructions and condition images into one representation. VAE: 64-channel RGBA autoencoder with 16x spatial compression and native transparency. Scheduler: Flow Matching with Euler discrete scheduling and dynamic shifting. The attention mask is where the speed comes from. Text tokens use a token-level causal mask. Image tokens use a chunk-level bidirectional mask within each image. Qwen calls this mixed-granularity attention. The condition prefix sits before the noisy latent, so it never attends to it. Its keys and values therefore stay fixed across denoising steps. The model computes text and input images once, at the first step. It reuses that prefix KV cache for every remaining step. Savings grow with the number of reference images, which explains the multi-image speed claim. What It Can Do Native transparency: Generates RGBA images from text, edits transparent layers, and extracts subjects from photos. Qwen recommends a fixed prompt template for transparent output. Multi-reference editing: Accepts up to 10 reference images. README examples include a group photo from 6 portraits and an outfit from 5 references. Local control: Edits can target regions using circles, painted annotations, or separate masks. Identity is preserved for people and products. Native 2K: Defaults to 2048 x 2048, with 7 supported aspect ratios up to 2752 x 1536. Aesthetics: Improved typography, portrait lighting, and fine detail. Qwen highlights panoramas, infographics, storyboards, and virtual try-ons. Benchmark: Qwen’s Own Chart The research team compares models on Qwen-Image-Bench, Qwen’s in-house benchmark. On that chart, Qwen-Image-2.1 scores 60.28 overall. That places it above Nano Banana 2.0 at 59.82 and every listed open-weight model. FLUX 2 Max, a 32B open model, sits at 55.33. 6 closed models score higher, led by GPT Image 2.5 Sunburst at 67.01. Interactive Explainer Running It Install PyTorch 2.4.0 or later, transformers 5.17 or later, Diffusers from source, accelerate, and pillow. Then: Copy CodeCopiedUse a different Browser import torch from diffusers import QwenImage21Pipeline pipe = QwenImage21Pipeline.from_pretrained( “Qwen/Qwen-Image-2.1”, torch_dtype=torch.bfloat16 ).to(“cuda”) image = pipe( prompt=”A neon shop sign that reads “QWEN IMAGE 2.1″, rainy night”, num_inference_steps=40, ).images[0] image.save(“t2i.png”) The same pipeline handles editing when you pass image= with 1 or more references. On smaller GPUs, pipe.enable_model_cpu_offload() reduces memory pressure. For serving, vLLM-Omni adds FP8 quantization, prefix KV caching, CUDA Graph decode, and tensor parallelism. SGLang adds Cache-DiT, CUDA graphs, multi-GPU parallelism, and component offload. ComfyUI ships native nodes and converted weights. Beyond NVIDIA, the release covers AMD Radeon GPUs via ROCm and 8 chip platforms via FlagOS. Qwen team also released 2 prompt-rewriting models, fine-tuned Qwen3.5-VL 9B checkpoints for text-to-image and editing. They expand short prompts into detailed ones and can pick an aspect ratio. Key Takeaways Qwen-Image-2.1 unifies generation and editing in a 7B DiT with a Qwen3-VL 8B encoder. Native RGBA output and up to 10 reference images come from one checkpoint. Prefix KV cache reuse computes text and reference images once per generation. It scores 60.28 on Qwen’s own benchmark, first among listed open-weight models. The Qwen Research License bars commercial use without a separate agreement. Check out the Model Weights, GitHub Repo, and Technical Details. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Alibaba Qwen Releases Qwen-Image-2.1: A 7B Open-Weight Model for Image Generation and Editing appeared first on MarkTechPost.

Many developers find that an agent idea works inside Claude Code or Codex, then struggles once they rebuild it with their own loop. The Strands Agents team at AWS is targeting that gap with Strands harness, a fully assembled, general-purpose agent harness. It runs locally or deploys to a cloud provider, ships for Python and TypeScript under Apache 2.0, and starts with one line of code. The team reports 28% lower cost than other harnesses running the same Claude or GPT models across 6 benchmarks, with near-equal accuracy. Is it deployable? Yes. It runs locally, and a bundled skills file helps your coding agent generate deployment config for AWS, GCP, Azure, Cloudflare, and Modal. What is Strands Harness A harness is the system around the model: the loop, tools, context handling, memory, and recovery. Strands already exposed those building blocks through the Strands Harness SDK. Strands harness packages them into working defaults. It is built as a general-purpose agent, not a coding agent. Out of the box, create_harness() returns an agent that: Runs on a current reasoning model through Amazon Bedrock, Anthropic, OpenAI, Google, Ollama, or LiteLLM. Ships shell, file (read, write, edit), and web tools, instead of a bespoke tool per task. Offloads bulky tool results to files and caches reused parts of each request. Keeps long-term memory across runs and resumes a conversation from a session ID. Delegates open-ended subtasks to a built-in helper agent and tracks multi-step work with a checklist. Loads Agent Skills when it finds them. Benchmark Setup and the 28% Figure The Strands Agents team ran distributed benchmarking on Amazon EC2 with Harbor, the evaluation framework from the Terminal-Bench creators. The score is the average across 6 benchmarks: ALFWorld, ContextBench, GAIA, WebShop, τ²-bench, and Terminal-Bench 2.1. Cost is the average dollars per task. Rivals on the chart are Claude Code, Codex, oh-my-pi, OpenCode, and DeepSeek Harness. One important thing to note. DeepSeek Harness was the most token-efficient harness overall, running about 14% cheaper than Strands harness. It also scored lower on every benchmark. The chart footnote states that including it brought the overall savings figure down to 28%. The highest-scoring point on the chart is Claude Opus 5 on Strands harness, near 85%. Terminal-Bench 2.1: Same Model, 5 Harnesses The clearest head-to-head uses Claude Fable 5 on Terminal-Bench 2.1, with 89 trials per harness. Harness Run cost Accuracy Strands harness $56.29 69.7 Oh-my-pi $86.83 69.7 OpenCode $73.42 66.3 Claude Code $248.05 61.8 DeepSeek Harness $40.30 59.5 Against Claude Code, Strands harness cost 77% less and scored 7.9 points higher. Oh-my-pi matched its 69.7 accuracy at 54% higher cost. DeepSeek Harness was cheaper still, but trailed by 10.2 points. The team also noted that 2 other open-source harnesses performed well on cost and accuracy against Claude Code. What Drives the Efficiency Strands harness ships defaults for prompt caching and context management. The team says context management largely drove both token efficiency and accuracy. 3 rules do the work: Tool results over about 1,500 tokens get truncated. Summarization (compaction) triggers when context usage passes 85%. Context recovery runs inside the loop if the window overflows. This matches recent independent research. The HarnessTax study compared Claude Code, Codex CLI, and Pi across 7 models. It found harness choice barely moved success rates, while the same model reached similar success at up to 5x the cost. The Strands researchers say a follow-up paper on their benchmarks is coming. Getting Started Install with pip install strands-harness or npm install @strands-agents/harness. Pick a model by name, or point the harness at a local Ollama model: Copy CodeCopiedUse a different Browser from strands_harness import create_harness agent = create_harness(model=”litellm/openai/gpt-5.6-sol”) agent(“Research the top three vector databases and compare their pricing”) The Strands CLI (npm install @strands-agents/strands-cli) lets you prototype an agent in plain English. In the team’s demo, the agent was asked to add the Playwright MCP server and measure video load latency on a blog post. Running /export then produced the harness code, with the Playwright MCP included, as a Python or TypeScript zip. The CLI itself is built on Strands harness. Strands engineer Gautam Sirdeshmukh also used it to build a desktop app that starts Strands harness runs remotely. Customization goes deep. You can override any default, swap models, add tools, or replace components down to the Strands Harness SDK. Because the harness is a library dependency, the agent prototyped on a laptop is the same one embedded in production. Key Takeaways Strands harness packages AWS’s Strands primitives into a general-purpose, Apache 2.0 agent. It reports 28% lower cost than rival harnesses across 6 benchmarks at comparable accuracy. With Fable 5 on Terminal-Bench 2.1, it cost 77% less than Claude Code and scored higher. Context defaults drive the gains: 1,500-token truncation, 85% compaction, in-loop recovery. One create_harness() call targets Bedrock, Anthropic, OpenAI, Google, Ollama, or LiteLLM. Check out the Technical details, GitHub repo, PyPI package, and Strands Agents docs. The post AWS Strands Agents Team Releases Strands Harness: An Open-Source Agent Harness With 28% Lower Token Cost at Comparable Accuracy appeared first on MarkTechPost.

Qwen has released Qwen3.8-LiveTranslate, its next-generation real-time simultaneous interpretation model. It listens to live speech, with optional video frames, and returns translated text and speech while the speaker is still talking. The core change is a new Interleave architecture. Qwen reports gains in faithfulness, fluency, and conciseness, with average lagging (LAAL) dropping from 2.8 seconds to 2.3 seconds. The release also adds real-time speaker diarization, synchronized bilingual display, and long-context disambiguation. Deployable? Yes, as a hosted API. It is live on Alibaba Cloud Model Studio and QwenCloud as qwen3.8-livetranslate-flash-realtime over WebSocket. What Changed Under the Hood Simultaneous interpretation is a tradeoff. Waiting longer gives the model more context. Speaking sooner cuts delay for the listener. Qwen3.8-LiveTranslate rebuilds this loop with an Interleave architecture. The latency metric here is LAAL, or Length-Adaptive Average Lagging. It measures how far the translation trails the source speech on average. It also avoids rewarding systems that over-generate output. A drop from 2.8 seconds to 2.3 seconds is roughly an 18% cut in average lag. QwenCloud team describes the model as the real-time version of Qwen3.8-LiveTranslate-Flash. It builds on the Qwen-Omni stack, large-scale multimodal data, cross-language and cross-modal alignment, and visual enhancement. The Flash model also supports offline audio and video translation. Three New Capabilities Real-time speaker diarization: The model distinguishes speakers in multi-party speech. It also preserves each speaker’s voice through more stable voice cloning. The API exposes cloning modes, including an always mode that re-clones before each response for multi-speaker sessions. Synchronized bilingual display: Source text and translation appear on screen together. In the API, source transcription streams as its own events next to the translation stream. Long-context disambiguation: The model uses conversation history to resolve names and terminology. A name introduced early in a meeting stays consistent later in the translation. Try the explainer below. It walks through the interleaved stream, speaker tagging, context disambiguation, language coverage, and session cost. Languages, Inputs, and Vision The model understands 60 languages. It can speak 29 of them, returning audio plus text. The remaining 31 return text only. Speech output covers Chinese, English, Arabic, German, French, Spanish, Japanese, Korean, Hindi, and others. Inputs are audio and optional images. Outputs are text and audio. Visual cues such as lip movements, gestures, and on-screen text help in noisy rooms and with ambiguous words. The docs recommend sending no more than 2 images per second. Teams can also set hotwords. These map source terms to fixed target translations. The docs recommend configuring no more than 1,000 hotwords. API, Pricing, and Limits Developers connect through the WebSocket Realtime API with the model ID qwen3.8-livetranslate-flash-realtime. The default turn detection type is speaker_detection. Clients stream audio continuously and receive server-generated responses. Default audio is 16 kHz PCM in and 24 kHz PCM out. The default voice is Tina. Set session.output_modalities to text only, or text and audio. Always send session.finish before closing, or the final segment is lost. Singapore list pricing per 1M tokens: Audio input: $7.50 Image input: $0.55 Text output: $20 Audio output: $30 Beijing pricing is lower, at $5.653, $0.466, $14.133, and $22.613 in USD. Audio input consumes 7 tokens per second. Audio output consumes 12.5 tokens per second. One hour of speech in and speech out costs about $1.54 in Singapore, before text and image tokens. The context window is 53,248 tokens, with 49,152 for input and 4,096 for output. Default rate limits are 10 requests and 100,000 tokens per minute. Model Studio lists function calling, structured outputs, batch inference, and fine-tuning as unsupported. Key Takeaways Qwen3.8-LiveTranslate cuts average lag (LAAL) from 2.8s to 2.3s. A new Interleave architecture improves faithfulness, fluency, and conciseness. It adds speaker diarization, bilingual display, and long-context disambiguation. It understands 60 languages and speaks 29. Access is API-only via Alibaba Cloud Model Studio and QwenCloud. Check out the Technical Details. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Alibaba Qwen Team Releases Qwen3.8-LiveTranslate: A Real-Time Interpretation Model That Cuts Average Lag to 2.3 Seconds Across 60 Languages appeared first on MarkTechPost.

Flet is an open source Python framework that renders its UI with Flutter. You write Python, and Flet draws Material and Cupertino widgets on iOS, Android, Windows, macOS, Linux and the browser. No Dart, Swift, Kotlin or JavaScript required. Last week, the Flet team released Flet 1.0 and declared it ready for building production apps. The release lands roughly four years after the project started. Is it deployable? Yes, today. Flet 1.0.0 is on PyPI under the Apache 2.0 license, requires Python 3.10 or newer for the SDK, and installs with pip install ‘flet[all]’. flet build produces artifacts for Windows, macOS, Linux, iOS, Android and web. The CLI accepts eight target platforms: apk, aab, ipa, ios-simulator, windows, macos, linux and web. The Test Matrix Flet runs framework unit tests across Python 3.10 to 3.14, alongside tests for the Flutter side. Control and example integration tests check behavior and compare screenshots, catching functional bugs and visual regressions. Python binary package tests exercise native libraries on Android and the iOS simulator, with the mobile pipeline building for Python 3.12, 3.13 and 3.14. flet build integration tests compile apps across those Python versions for all six platforms, and flet test launches a packaged app and drives it on all five native platforms, including Linux ARM64. You can write the same kind of tests. Integration tests for your own app use pytest, run via flet test against the packaged build, with screenshot comparison on Android and iOS. Python Versions and Mobile Libraries Flet bundles Python 3.12, 3.13 or 3.14 with your app, and web builds use the corresponding Pyodide release. See how to choose a Python version. The Flet package index now lists more than 100 packages, including NumPy, pandas, Matplotlib, Pillow, SciPy, scikit-learn, cryptography and pydantic-core, plus supporting native libraries. The mobile-forge pipeline automates wheel builds for iOS and Android. Availability still depends on the package and the target. Performance and Packaging Flet now tracks changed properties and skips unnecessary comparisons during UI reconciliation, with 0.83 benchmarks measuring up to 6.7x improvement in control diffing. In packaged native apps, dart-bridge lets the Python and Dart runtimes talk inside one process, without sockets, with dedicated channels for binary data. Packaging enables bytecode compilation by default, and redesigned Android packaging loads Python packages directly from the APK without extraction. Declarative UI Flet Declarative describes UI as a function of application state, organized into reusable components. Flet Studio and the Flet mobile app are themselves declarative Flet apps. The imperative style remains supported. Compare both in the docs. Compatibility and AI Tooling The compatibility policy deprecates APIs before removal, with a default deprecation period of three minor releases. The Flet MCP server gives AI coding assistants version specific Flet API information, plus tools for finding examples, icons and CLI options. Flet Studio runs in the browser with a built in AI agent, and projects can be downloaded for local development. Interactive Explainer The embed below walks through five mechanics: the build pipeline across eight targets, declarative versus imperative state, the CI test layers, runtime bundling with mobile wheels, and the event loop trap. Colors follow Flet’s own brand tokens. Key Takeaways Flet 1.0.0 ships on PyPI under Apache 2.0, Python 3.10 or newer for the SDK. flet build targets eight platforms: apk, aab, ipa, ios-simulator, windows, macos, linux, web. Bundle Python 3.12, 3.13 or 3.14, with 100 plus packages available for mobile. Control diffing measured up to 6.7x faster, and dart-bridge removes socket overhead. Upgrading from 0.28 is a real migration: handlers now run on one event loop. Check out the Flet 1.0 release announcement. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Flet 1.0 Released: Build Production Web, Desktop and Mobile Apps in Python Only appeared first on MarkTechPost.