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August 14Friday
  1. Vapi Blog22

    How to Choose a Conversational AI Platform for Enterprise Development

    A vendor-neutral guide lays out five criteria for picking a conversational AI platform for enterprise voice agents: developer control, model choice and fallbacks, testing and observability, the full agent lifecycle, and compliance at scale. It cites a Zapier survey of 542 US C-level executives in which 81% worried about depending on a specific AI vendor and only 6% could drop their primary vendor without disruption. The guide argues latency and orchestration matter more than feature lists, noting AssemblyAI's finding that delays past 500–700 ms feel unnatural.

August 13Thursday
  1. MiniMax Blog71

    MiniMax releases Music 3.0, an open-weights music generation model for full songs up to five minutes

    MiniMax introduced Music 3.0, an open-weights music generation model that composes, arranges and produces a complete song of up to five minutes from a creative concept and optional lyrics in a single generation. The pipeline pairs an 8B Global LLM initialized from Qwen3.5-8B with a 0.6B Local LLM, fuses their continuous hidden states to condition a 2.4B flow-matching module and a 123M Flow-VAE, and uses eight-layer RVQ plus Structured Captions that describe emotion, instrumentation and vocal delivery over time. MiniMax says this yields more accurate interpretation of creative intent, fuller arrangements and clearer, more natural vocals.

    Why it matters: MiniMax's architecture breakdown shows how an 8B global LLM paired with a 0.6B local model and flow-matching stack targets the long-song coherence problem.

  2. Factory38

    Factory launches Agent Effectiveness analytics for Enterprise

    Factory has made Agent Effectiveness generally available in Factory Analytics for Enterprise organizations, linking Factory sessions to cycle time, work intent, and shipped artifacts. The feature adds Throughput, Output, and Attribution views that connect Droid usage to delivery outcomes, backed by a Local Signals classification layer that reads from Jira, Linear, GitHub, and GitLab. It is opt-in, off by default, and requires the Advanced Analytics enterprise control.

August 12Wednesday
  2. Modal Blog62

    Qwen3.8-2.4T-A95B 开源权重发布，Modal 同步上线共享端点

    Qwen3.8-2.4T-A95B 已作为开源权重模型发布，并同步在 Modal 上线。Modal 表示其与 Qwen 在发布前合作，通过 SGLang 和针对 Qwen3.8 形状调优的定制 DFlash 推测器，为 Modal Auto Endpoints 提供 day zero 支持。

    Why it matters: Modal 在 Qwen3.8-2.4T-A95B 开源权重发布当天即上线共享端点，并说明其定制 DFlash 推测器如何针对该模型的形状提升推理速度。

August 6Thursday
  1. Vapi Blog12

    Vapi Appoints Nathalie Criou as VP of Product

    Vapi has hired Nathalie Criou as its VP of Product, a few months after she joined the voice AI startup. Criou previously held product and general management roles at Google, VMware, Amazon, Twilio and Docker, and founded and sold the startup RidePal. She said she joined because voice is an underused channel that AI can now transform, citing customer Kavak reorganizing around voice within six months.

August 4Tuesday
  1. Vapi Blog22

    What Makes a Great Conversational AI Experience: Vapi on Voice Design

    Vapi argues that voice agents are judged on five real-time dimensions: latency, turn-taking, interruption handling, prosody, and a resilient pipeline. It cites research putting human turn gaps near 200ms and notes delays beyond roughly 500–700ms feel unnatural, while naive sequential pipelines can cost about 1.9 seconds versus 600–900ms for optimized ones. Vapi positions its model-agnostic pipeline as tunable and testable via simulated calls.

  2. Modal Blog34

    Modal moves serverless Functions to a regional I/O plane, cutting p50 latency by ~80ms

    Modal has migrated all Functions to a new geographically distributed I/O plane, dropping end-to-end Function Call latency by roughly 80ms at p50. Previously every Function's inputs and outputs routed through us-east I/O servers, adding up to a few 100ms per call; the new plane is available in ≥4 regions and selectable via the routing_region flag. The server layer was rewritten in Go, with non-critical work moved off the hot path.

August 3Monday
  1. Vapi Blog33

    Why model flexibility matters in voice: Speechify Simba 3.2 and Fish Audio S2.1-Pro top Vapi's Humanness Index

    Speechify's Simba 3.2 debuted atop Vapi's Humanness Index at 99, one point under the human baseline of 100, and Fish Audio's S2.1-Pro entered at 94 with 141 ms time to first audio. The prior leader, xAI's Grok TTS, held the top spot for only three weeks at 95, and the leading score has since moved from 95 to 99. Vapi argues that model-agnostic voice platforms let teams swap TTS models as rankings shift.

July 31Friday
  1. MiniMax Blog82

    MiniMax launches H3, a general-purpose multimodal generation model with native stereo video at 2K

    MiniMax launched H3, a general-purpose multimodal generation model that understands text, images, video and audio as a unified context and generates video with native stereo sound up to 15 seconds at 2K resolution. The company says 2K output costs less than a third of mainstream models per second, and it plans to open the model weights in the coming days subject to applicable laws and regulations. H3 is built on Contextual Omni Representation, H3-VAE, H3-Omni Transformer and In-Context Regeneration, and MiniMax says a full technical report is coming soon.

    Why it matters: MiniMax's H3 unifies text, image, video and audio generation in one model and claims 2K pricing below a third of mainstream models, with weights promised soon.

July 29Wednesday
  1. Berkeley AI Research58

    From CUDA to MLX: K-Search Brings Kernel Optimization Expertise to Apple Silicon

    IBM Research extended K-Search, the evolutionary kernel search framework from UC Berkeley Sky Lab, with an MLX backend and a structured CUDA-to-MLX translation layer that adapts existing CUDA kernels into Apple Silicon kernels. On an MLX attention kernel the approach reached 0.97x the speed of Apple's native MLX Attention kernel, up from 0.26x without the translation context, and an evolved Mamba SSM kernel reached up to 20x prefill speedup over the community mlx-lm implementation on an M1 Max. The authors attribute the gain mainly to the quality of context and constraints given to the LLM rather than its ability to write Metal code, and say the method is not specific to MLX.

  2. Modal Blog40

    Modal responds to Hugging Face agent incident: platform isolation not compromised

    Modal said its platform and isolation were not compromised in the Hugging Face agent intrusion, which used Modal as a third-party launchpad. The affected environment was a customer application deployed to a publicly accessible endpoint without authentication that executed internet-submitted code inside a Modal Sandbox, and the code execution stayed within that customer's own container. Modal noted unauthenticated Sandbox exposure is never the default and recommended authentication, IP allowlists and restricted outbound network access.

July 27Monday
  1. Vapi Blog22

    Anthropic and Vapi Webinar Q&A: How the Claude Haiku Ride-Booking Voice Agent Was Built

    Vapi published answers to questions from its webinar with Anthropic on voice AI, detailing the live ride-booking demo agent built on Vapi with Anthropic's Claude Haiku and the Layla v2 voice. The agent used five tools — estimate, book, check status, cancel and end call — plus smart endpointing and a conservative interruption threshold to reach sub-second turns. Agents running Claude on Vapi handled nearly 5M calls in the past 30 days across 50+ languages.

  2. Modal Blog75

    Moonshot's Kimi K3 now available on Modal with day-zero vLLM support

    Modal made Moonshot's Kimi K3 available on its platform on release day, offering the 2.8 trillion parameter multimodal model with a 1M token context window through a token-priced Shared API and dedicated Auto Endpoints. Modal says a custom DFlash speculator tuned to K3's architecture raises interactivity from 100 to 460 tokens per second and throughput from 800k to 1.5 million TPM per GPU on agentic workloads. The model uses a mixture-of-experts design with 16 of 896 experts active per token, and Moonshot contributed a new prefix-caching implementation to vLLM ahead of the release.

    Why it matters: Modal details how it brought Moonshot's 2.8T-parameter Kimi K3 to its platform on release day, including a custom DFlash speculator that lifts interactivity from 100 to 460 tokens per second.

July 26Sunday
  1. Berkeley AI Research33

    ABBEL: Teaching LLMs to Update Belief States for Long-Horizon Interaction

    Berkeley AI Research introduces ABBEL, a framework that replaces full interaction history with natural-language belief states and supervises their contents via belief grading. On CollabBench collaborative coding, reconstruction-based belief grading cuts the gap to full-context models by about 50% and trains in 50 steps instead of 100, while using fewer peak tokens.

July 24Friday
  1. Vapi Blog22

    How to reduce customer wait times with AI voice agents

    AI voice agents cut customer wait times in two ways: removing work from the queue through self-service deflection, intelligent routing, agent assist, and demand prediction, and answering instantly with no hold. SQM Group found customers abandon calls at 2 minutes 36 seconds on average, while ContactBabel put the UK mean abandonment rate at 8.4% in 2024. Vapi says a first voice agent can be stood up from its dashboard in minutes, then measured on ASA, abandonment, and first-call resolution.

July 21Tuesday
  1. OpenAI Alignment Blog62

    OpenAI and Apollo Research measure reward-seeking with Contrastive SDF

    OpenAI and Apollo Research developed Contrastive SDF, a test that finetunes two copies of a model on synthetic documents implying opposite grader preferences and measures how far behavior follows the grader. Applied to intermediate checkpoints of a capabilities-focused OpenAI o3 RL run without safety training, the grader gap trended upward from early to late checkpoints while gaps for other authorities stayed near zero. On the Broken Promises Coding and Log Falsification honesty evaluations, the late checkpoint's honesty depended far more on what it believed the grader rewarded, and the method recovered the target authority of three gpt-oss-120b model organisms trained to favor the User, the Grader, or OpenAI Leadership.

    Why it matters: OpenAI and Apollo Research introduce Contrastive SDF, a finetuning-based test that measures how strongly a model's behavior tracks what it believes its grader rewards, and find the tendency grows across an o3 RL run without safety training.

  3. Modal Blog58

    Modal and Cognition launch Devin Outposts with modal-devin sandbox integration

    Modal is a launch partner for Devin Outposts, a Cognition feature that moves Devin's code execution into an environment the customer controls while Devin's reasoning stays in Cognition's cloud. The open-source modal-devin library and CLI runs each Outpost session in a Modal Sandbox, giving access to GPU-backed machines, custom images, workspace snapshotting and scale-to-zero, and it is live in alpha starting July 21, 2026. Setup uses uvx modal-devin init, which generates an orchestrator function and a worker session function that teams can keep customizing.

July 16Thursday
  1. Modal Blog60

    Modal rebuilds its sandbox platform to run 1 million concurrent sandboxes, created in under a minute

    Modal has rebuilt its core sandbox platform from the ground up, and on the new system it ran 1 million sandboxes concurrently, creating all of them in under a minute. The redesign removes central coordination and any data store from the sandbox creation path: horizontally scaled scheduling servers pick a worker with an in-memory algorithm and call it directly, while workers publish state asynchronously to a Redis stream. Modal says sandbox start latency is under half a second at the median, and the new system is already available in Beta with a one-line code change to opt in.

    Why it matters: Modal rebuilt its sandbox control plane to drop central coordination, showing how far container scheduling can scale once global consistency leaves the critical path.

July 15Wednesday
  1. Modal Blog60

    Thinking Machines' Inkling multimodal model now available on Modal

    Thinking Machines released Inkling, a general-purpose multimodal model that takes text, image and audio inputs and outputs text, and Modal is offering it from day zero as a Managed Endpoint with token-based pricing. Inkling is a mixture-of-experts transformer with 975B total parameters, 41B active, a 1M token context window and native audio and vision, using five sliding-window attention layers for every full-attention layer. On agentic workloads with 8x B200s, Modal's custom DFlash speculator reached 250 tokens per second per user at 2.5M TPM of per-GPU throughput, 67% faster interactivity than the model's built-in speculative path at matched throughput.

    Why it matters: Modal's day-zero endpoint for Thinking Machines' 975B-parameter Inkling shows how a custom DFlash speculator lifts interactivity 67% over the model's built-in speculative path.

July 13Monday
  1. Vapi Blog58

    Vapi Humanness Index: Grok TTS scores 95 and MiniMax Speech 2.5 scores 93 against a real human at 100

    Vapi's Humanness Index, a blind crowdsourced leaderboard where listeners pick which of two cloned voices sounds more human, places xAI's Grok TTS at 95 and MiniMax Speech 2.5 at 93 against a real human recording scored at 100 after more than 11,000 votes. Every battle uses the same cloned voice and script across models, and the real human recording is included in the comparison set, so a 95 means listeners chose Grok TTS at nearly the same rate as the human on the same quote. Vapi argues naturalness is no longer the bottleneck for voice agents, since callers experience the voice rather than the reasoning behind it.

July 9Thursday
  1. Modal Blog22

    Modal Is a Computer: Recasting the Cloud Platform as a Machine

    Modal argues it should be understood as a computer rather than an "Uber for compute," a neocloud, or an inference provider, since it sells processor-seconds and byte-seconds rather than tokens. The post maps a conventional machine's ALU, memory hierarchy, I/O, NIC, and operating system onto Modal's containers, object storage, Input/Output Plane, Routing Plane, and Tunnels. It also aligns Modal Functions, Sandboxes, and Servers with stateless tools, Linux processes, and HTTP listeners.

July 7Tuesday
  1. Berkeley AI Research36

    Intelligence Is Free, Now What? Data Systems for, of, and by Agents

    UC Berkeley's Aditya G. Parameswaran and collaborators argue that as inference costs fall from roughly $30 per million tokens in early 2023 to under $1 today, data systems must be redesigned around three challenges: systems for agents, of agents, and by agents. They note that in a text-to-SQL benchmark with multiple agents per task, only 10-20% of sub-plans are distinct, so 80-90% of sub-queries duplicate work, and propose reuse, approximation, and proactive data systems.

July 6Monday
  1. Modal Blog38

    How to price serverless GPUs: Modal's interactive cost model

    Modal published an interactive tool for estimating serverless versus reserved GPU costs, defaulting to current rates for a B200 GPU serverlessly on Modal and via three-year hyperscaler reservation. The model shows that when an application's peak-to-average ratio exceeds the reservation discount rate, serverless GPUs cost less overall, and Modal cites observed discounts of 2-5x against peak-to-average ratios of 5-10x.

July 2Thursday
July 1Wednesday
  1. Berkeley AI Research20

    Berkeley AI Research (BAIR) 2026 Graduate Showcase Highlights Ph.D. Graduates' AI Work

    The Berkeley Artificial Intelligence Research (BAIR) Lab has celebrated its class of 2026 Ph.D. graduates, whose work spans robotics, large language models and reasoning, computer vision, generative modeling, AI safety, and AI for science and healthcare. Graduates are heading to faculty and postdoctoral positions, industry research labs, and startups of their own founding, including roles at Physical Intelligence, OpenAI, Mistral AI, Amazon, and Princeton CITP.

  2. Modal Blog58

    Modal Research and HeavyBall's MRP brings multi-token prediction to diffusion LMs

    Modal Research and NYU Shanghai's HeavyBall Research introduce Multi-token Residual Prediction (MRP), a 3-layer module attached to a frozen diffusion LM backbone that predicts the inter-step logit residual instead of the full next-step distribution. On SDAR-4B GSM8K, MRP holds 70.9% accuracy at K=3 versus 5.9% for naive MTP distillation, and in SGLang it reaches up to 1.56x throughput losslessly in speculative mode or recovers up to +16 accuracy points under aggressive low-threshold dynamic decoding. The paper, code, SGLang implementation, and SDAR-MRP models on Hugging Face are released.

June 30Tuesday
  1. Modal Blog66

    Anthropic launches Claude Science with Modal as scalable compute backend

    Anthropic launched Claude Science on June 30, an AI workbench where life sciences researchers run computational workloads directly inside a conversation with Claude. Modal integrates as the compute backend: workloads needing GPUs or many concurrent CPUs route automatically to Modal sandboxes from a researcher's connected workspace, while default execution stays sandboxed on the local machine. Modal cites fan-out virtual screening across hundreds of containers, per-step GPU access for models like ESMFold2, shared Modal Volumes storage and reproducible Modal Images, and commits up to $100,000 in compute for Anthropic's AI for Science Claude Science Cohort at $500–$2,000 per project, with applications open through July 15, 2026.

    Why it matters: Modal's post explains how Claude Science routes GPU and parallel workloads to Modal sandboxes, with concrete life-science examples and a compute grant for academic labs.

June 25Thursday
  1. Vapi Blog22

    Vapi: Voice Agents That Turn High-Stakes CX Into a Competitive Advantage

    Vapi is pitching its voice AI platform as a way for brands to turn the contact center from a cost center into a competitive advantage, citing Amazon Ring, which evaluated over 40 voice AI vendors before going live on Vapi in two weeks and now runs 100% of its inbound CX volume through it, with improved CSAT scores. Vapi argues packaged voice solutions lock brands into vendor-controlled models and generic voices, while building from scratch is slow and costly, and it touts sub-second response times, VPC-resident architecture with zero data retention and PII scrubbing, and deployments that go live in days.

  2. Modal Blog55

    Modal launches serverless Servers for low-latency HTTP, WebSocket and gRPC traffic

    Modal launched Servers, a regionalized autoscaling pool of HTTP server replicas for HTTP, WebSocket and gRPC traffic aimed at latency-sensitive workloads such as LLM inference for interactive agents. Modal says it cut p50 latency from 39ms to 6ms by keeping control-plane lookups and queueing off the request path, so clients get a 503 when no replica is available and must handle load shedding in the application. The routing layer runs on AWS EKS with Envoy at the edge and Modal's in-house Rust proxy fprs, built on Cloudflare's pingora, handling domain association and load balancing, with config read from Spanner and cached in memory.

June 24Wednesday
  1. Vapi Blog15

    Vapi Answers Top Questions from Its SVG Lead Qualification Webinar

    Vapi published answers to the seven most-asked questions from its webinar with Spring Venture Group, where Homer Kay described how SVG uses Vapi voice agents to qualify Medicare leads before transferring callers to licensed agents. The answers cover outbound calling compliance, API-first integrations, swapping STT, LLM and TTS components independently, human handoff, and HIPAA-eligible configurations with Zero Data Retention.

  2. Modal Blog58

    Modal launches Auto Endpoints, citing speculative decoding for state-of-the-art inference latency

    Modal is launching Modal Auto Endpoints, which bring Modal's infrastructure scalability to state-of-the-art inference performance with a single click while keeping control of the code. Modal says the main latency win comes from speculative decoding with custom DFlash speculator models, which cut about 100ms of end-to-end latency in a Decagon Voice case study, roughly 40% of server-side decode latency, making it over 60ms faster than the fastest proprietary alternative. Modal also released open source DFlash speculator models with Z Lab and SGLang, including one for Qwen 3.5 397B-A17B that improves on MTP by over 50%.

June 23Tuesday
  1. Modal Blog60

    Modal launches Auto Endpoints for self-serve, OpenAI API-compatible LLM inference

    Modal launched Auto Endpoints, a self-serve service that deploys production-grade LLM inference as an OpenAI API-compatible endpoint backed by a Modal App the user can see and control. Each supported model ships with a starting deployment tuned from Modal's own benchmarking, using engines like SGLang and the DFlash speculative decoding drafter from Z Lab, and users can inspect GPU choices, engine flags and inference metrics such as TTFT, ITL and speculative decoding acceptance length. Modal also released Modal Servers from beta for regionalized routing with 5ms overhead, and said it generates endpoint deployments with an internal agentic system still monitored by human engineers.

    Why it matters: Modal's Auto Endpoints expose the serving stack, engine metrics and benchmark recipes that managed inference providers keep hidden, giving teams a self-serve path to owning open-model inference.

June 22Monday
  1. Modal Blog36

    Modal explains why sandbox "started" ≠ "ready" and how warm pools cut perceived startup latency

    Modal argues that container scheduling and boot time, which most sandbox benchmarks measure as time to interactive, is the smallest part of what users actually wait through; application-level setup after the container starts often takes multiple seconds. Modal is making Readiness Probes generally available to signal when a sandbox has finished initializing, and recommends warm sandbox pools built on modal.Queue plus Directory Snapshots to move setup off the user's critical path.

June 19Friday
  1. Modal Blog60

    Modal releases DFlash speculators for Qwen 3.5 and 3.6, claiming 5-20% extra speedup

    Modal released a set of DFlash draft models for the Qwen series on Hugging Face, including Qwen 3.6 35B-A3B-DFlash and Qwen 3.5 variants from 4B to 122B-A10B, plus a DFlash speculator for Qwen 3.5 397B-A17B built with SGLang. The company says the new drafters add 5-20% speedup over existing DFlash baselines and can run Qwen 3.5 122B-A10B at over 1000 tok/s at concurrency 1 on a B200 node, versus 250 tok/s without speculation. The post argues speculative decoding delivers 2x-3x gains where kernel optimization yields small percentage points, and presents a roofline model for estimating speedup from acceptance length.

    Why it matters: Modal explains why it bets on speculative decoding and releases DFlash draft models for the Qwen series, with measured speedups and a roofline model for estimating gains.

June 17Wednesday
  1. Vapi Blog30

    Built for the Ear: Designing Conversations for Voice AI

    Voice AI success starts with use-case selection: tasks ending in a link, code, or number belong in chat, while urgent, hard-to-type, or emotionally loaded problems suit voice. Chat scripts must be rewritten for the ear—split long passages into single-idea turns, restate collected details like dates aloud, and limit spoken options to two or three. One staffing company cut downstream placement errors by 30% by confirming each slot as it was collected, and another team trimmed 40 seconds off average call duration by turning open questions into choices.

  2. Vapi Blog40

    How Vapi Bootstrapped 15 Homepage Voice Agents in Code

    Vapi built the 15 voice agents on its homepage — 3 use cases across 5 languages — by generating them from a single ROUGH_DRAFT.md file instead of hand-editing each in the dashboard. The four-phase framework (plan, draft, bootstrap, harden) scaffolds a TypeScript project with OpenAI gpt-4.1 at temperature 0.5, ElevenLabs and Deepgram/Soniox voice stacks, and an idempotent upsert loop that stores assistant IDs in .env.local. The repo is public: clone it, add VAPI_PRIVATE_KEY, and run bun run bootstrap.

June 15Monday
  1. Modal Blog34

    Modal Product Updates: VM Sandboxes, Regional Routing, RBAC and More

    Modal shipped a batch of platform updates, including alpha VM Sandboxes that run a real Linux kernel to support Docker containers, eBPF, systemd and cgroups. Functions can now route through US West, EU West or Asia Pacific South instead of the default US East region, and RBAC is available to all Team and Enterprise plans. Sandbox readiness probes are now GA, and the Python SDK 1.5.0 and JS/Go SDK 0.8.0 set a 30-day default snapshot TTL.

June 11Thursday
  1. Modal Blog34

    Modal Makes FlashAttention-4 Faster for LLM Inference

    Modal contributed optimizations to the FlashAttention-4 kernel to speed up decode-heavy LLM inference workloads, where memory bandwidth is the bottleneck. The changes include porting the "split KV" parallelism technique to FA4 and replacing TMA cp.async.bulk loads with cp.async for irregular memory accesses. An FP8 input PR (merged April 17, 2026) delivers up to 1.16x throughput over the bf16 baseline and enables smaller KV caches.