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Hacker News Show HN

Show HN: Open-source customer-facing agents

Hi, Dmitri from Radioso here, an open-source platform to design AI agents for customers and visitors in any channel, in the style of Fin/Ada/Decagon. The repo is https://github.com/radioso-ai/radioso and our website is https://radioso.aiToday, your choices are:1. Buy a very expensive service for your vertical (usually starting with $$ five figures monthly) 2. Get a cheap off-the-shelf chatbot that mostly answers FAQs 3. Build your own, and then also build the inbox, human handoff, audit trail and integrations around itAt Radioso we wanted an open-source option that combines the best of all 3. You can integrate it with your own software via API/SDK or your CRM, or run it standalone, either self-hosted or in our cloud.The engine runs multi-step interactions and takes actions: calling your APIs, updating records, booking things. Maybe you’ve tried Parlant; Radioso is the alternative “with batteries included”: RAG, audit trail, customer service inbox, and integrations like Slack and email.Under the hood it’s plain TypeScript + PostgreSQL, works with OpenAI/Anthropic/other models, and is licensed under Apache 2.0 which includes all features (The only thing we keep under EE license is the multi-tenant layer we use to run our own cloud).We have existing customers running it for technical Q&A, course booking, online product recommendations and goods returns, among others, some reaching 92% of conversations automated. We’re currently working on voice support so we can offer a call center.Our ambition is to be “the WordPress of customer-facing AI” :), where people can contribute their favorite plugins and benefit from the ecosystem.Longer term, we think browsing the web will mostly mean talking to your own agent, and when you need a service or a purchase, your agent will talk to the vendor’s agent to get things done. For this, Radioso already publishes an A2A Agent Card and we’re working on the rest. Your Radioso services are also callable over MCP, for customers who already use it.Thanks for your attention. We’d especially love to hear which integrations you’d want, whether you’d self-host it, and what’s stopped you from deploying a customer-facing AI agent so far. Tell us what you think. Comments URL: https://news.ycombinator.com/item?id=50423820 Points: 1 # Comments: 0

llama.cpp releases

b12202

The llama.cpp v1.220.2 release introduces enhanced BF16 precision support across multiple components, aligning with reference implementations by moving specific operations like GLM4 family projections, MiMoVL vision matmuls, and Qwen3-TTS speaker encoder conv1d to BF16 where appropriate. Backends such as CUDA, Hexagon, OpenCL, WebGPU, and Vulkan now uniformly handle GGML_PREC_BF16 similarly to F32, while CPU, SpaceMIT, and ET backends extend flash attention support to BF16 with F32 accumulation. Flash attention tests now include BF16 cases, and the KQ multiplication in Llama is updated to request BF16 precision. A TODO remains in clip.cpp for future work.

Google DeepMind

Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips

Google DeepMind has launched Gemini Omni 1.2 Flash, introducing native 15-second video generation, improved reference adherence, stronger consistency across frames, multi-turn conversational editing, more natural speech, and cinematic camera movements. The model is now generally available via the Gemini API and Gemini for business, priced at $0.07 per second for 720p video—30% lower than its predecessor. It supports scalable workflows, including draft generation at lower resolutions, iterative edits, and upscaling to 1080p or 4K. Use cases include product explainers, social ads, virtual try-ons, and branded content, with applications across Google AI Studio, Google Cloud, Google Vids, Flow, the Gemini app, and Google Ads.

arXiv cs.CL

Holistic Parallel Supervision for Expressive Speech to Speech Translation

arXiv:2611.06010v1 Announce Type: new Abstract: Expressiveness is central to speech-to-speech translation (S2ST), yet existing datasets rarely align source and target speech jointly in content, prosody, timing, and speaker identity. Consequently, current systems often rely on indirect conditioning or transfer mechanisms. To obtain this supervision, we introduce a training-data construction strategy that combines professional dubbing, which provides human-performed correspondence in content, prosody, and timing, with multilingual voice conversion to reduce speaker mismatch. We evaluate this supervision using existing expressive S2ST systems and DirectS2ST, an end-to-end method developed in this work that predicts the complete target-speech representation directly from source speech. Experiments on English-German and English-Spanish show that holistic parallel supervision improves expressive realization across all evaluated systems. DirectS2ST exhibits the largest gains in content, prosody, timing, speaker similarity, and human-rated expressiveness. To our knowledge, this work provides the first systematic investigation of holistic parallel supervision for expressive S2ST.

arXiv cs.CL

Selective, Risk-Controlled Emotion Forecasting in Conversation: From a Persistence Barrier to Departure Detection with Conformal Guarantees

arXiv:2611.05896v1 Announce Type: new Abstract: Forecasting emotion in conversation is increasingly framed as predicting the next emotional state's value. We show this targets a hard quantity in a state-observed testbed and propose a learnable, deployable reformulation instead. On valence-arousal-dominance trajectories from a dyadic corpus, repeating a speaker's own previous state sets a persistence baseline that no frozen-encoder correction meaningfully improves upon and a state-space filter exceeds; the floor requires the historical state to be observed, since an estimated anchor collapses persistence below a direct forecaster. What survives is the timing of a departure from persistence, learnable on three of four corpora and near chance on the highest-base-rate corpus, a negative result we report. We recast the task as selective departure forecasting: a lead-time-weighted metric with a leakage protocol, and commitment policies whose best choice switches with the departure rate. A dialogue-level conformal risk control bounds the expected loss of committed predictions, meeting the target in all twelve configurations tested, though the guarantee is bought through abstention, with coverage and alarm recall falling sharply on the hardest corpus. One tested language model's confidence fails as a ranking signal where another succeeds, a model-dependent failure not yet attributable to family versus scale. Code and result logs are available from the corresponding author upon request.

Hacker News Show HN

Show HN: Phone Link for macOS – Use Your Android Phone on macOS

English isn’t my first language, sorry for grammar mistakes.I usually use Windows and Android, and these two platforms can be linked via Microsoft's "Link to Windows", or "Phone Link" program. So, you can easily use some features such as notifications, messages, screen mirroring, and clipboard synchronization on Windows from your Android phone.Recently, I changed my personal computer to macOS from Windows, and it made me need a similar program. I developed this program to run on macOS while still using the "Link to Windows" app on Android phone.It supports most features of the Windows client (notifications, messages, photos, mirroring, file transfer, etc.), except calling.The license is MIT. This project is an unofficial client unrelated to Microsoft and has no affiliation with them. Comments URL: https://news.ycombinator.com/item?id=50389540 Points: 1 # Comments: 0

Hacker News Show HN

Show HN: Repairer – close the breach when a Linux flaw goes public

As we've seen with the Ubuntu critical CVE-2026-98432 (CVSS 9.8), AI-dicovered zero day exploit are being reported at an exponential rate and we don't have the process for dealing with the flood of exploits in real time.What we need is an AI-based system for autopatching systems based on real time information. The vendor's fix can take days or weeks to be out (is the Ubuntu thing patched yet?) and meanwhile the grid is vulnerable to exploitation, especially AI worms.Repairer is a small program that runs on each of your servers. The day an exploit goes public it puts up a stopgap that blocks it, and once the real fix is up it takes the stopgap back off by itself.A stopgap only takes something away — drop certain packets, turn off a kernel module or service, remove a setuid bit. It never opens a port, installs or downloads anything, nor runs code. It arms an undo playbook before applying the stopgap, so the change reverts if affects any services.It only ever acts on rules you sign with a (hardware) crypto wallet.Let's go further: stopgaps are written and checked by a swarm of AI agents (gnosh.ai sessions) running on gno.land, each with bonded stake. Each stopgap is run on fresh machines, and whoever is caught lying loses their stake to whoever caught them.The agent is built and tested on Ubuntu and Debian, all open source, standard-library Go. More distros coming. Let's build repairer together.https://gno.security/repairer/ https://github.com/gnosecurity/repairer Comments URL: https://news.ycombinator.com/item?id=50388417 Points: 1 # Comments: 1

Hugging Face 官方博客

A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.

Phonon, a small text-to-speech model that runs entirely on the user's device without requiring internet access, has been released with support for English, French, German, Spanish, and Portuguese. Built on the Pocket TTS lineage using flow matching, Phonon achieves high-quality audio in compact sizes (65 MB and 75 MB), with the 31m version licensed under Apache 2.0 for commercial use. It excels in intelligibility and speed, ranking first in German and Spanish on Hugging Face's Open TTS Leaderboard and leading in CPU streaming benchmarks. The model supports voice cloning, custom voices via Gradium Studio, and integrates with Hugging Face Transformers. It is available in multiple formats and backends, including WebAssembly, GPU, and NPU, enabling deployment across phones, laptops, single-board computers, and browsers. Over 200,000 downloads were recorded in its first two weeks, with active developer adoption through tools like ExecuTorch, Micdrop, and Spaces. Future developments include support for additional languages, smaller models, and custom vocabulary fine-tuning.

The Verge · AI

My therapist thinks I should chuck the Amazon Bee Frontier out a window

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. "How does that make you feel?" I get asked this a lot in therapy. Plenty of life events, big and […]