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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. One Useful Thing60

    Ethan Mollick on the twilight of chatbots and the rise of agent-run work

    Ethan Mollick argues that AI use is shifting from chatbot co-intelligence toward long-running agents that need less human intervention, and that this change is happening fast. He cites METR, the UK AI Security Institute and GDPval benchmarks rising faster than exponential, plus an Epoch finding that Opus 4.7 worked alone for 14 hours to build a software package estimated at 2-17 weeks of human engineering for $251 in tokens. An OpenAI and academic economists study found a quarter of OpenAI workers run at least four agents weekly, with legal and HR adopting at nearly the same rate as coders, and a Claude Code study found domain expertise, not profession, predicted success.

  3. 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.

  2. Factory62

    Factory rolls out automated security review in Droid

    Factory is rolling out automated security review in Droid, which runs a STRIDE-based security review alongside standard code review on every non-draft PR and posts findings with severity, a CWE reference, an explanation and a suggested fix as inline comments on the diff. The company says Droid's security review has been run across production codebases and surfaced findings that went through responsible disclosure, including CVE-2026-42876 in external-secrets and workos/workos-node#1578. Droid builds a lightweight threat model of the changed surface, scans for vulnerabilities such as OWASP Top 10 and OWASP LLM Top 10 issues, then validates each candidate finding against the diff to filter false positives before posting one deduplicated summary per PR.

    Why it matters: Factory's Droid now runs a STRIDE-based security review on every non-draft PR, and the post shows real CVEs it surfaced in production codebases.

June 10Wednesday
  1. One Useful Thing78

    Ethan Mollick reviews Claude 5 Fable, the first public Mythos-class model

    Ethan Mollick tested Claude 5 Fable, the first Mythos-class model released to the public, and found it outperformed every other public model he has used by a considerable margin, running up to a dozen hours on multi-page specifications. In one Claude Code session it built an interactive isochrone map by spawning cheaper Claude Sonnet agents to research over 2,200 flights, rail schedules and road speeds, and in another it worked nine and a half hours to produce Concord, software for calibrating human and AI judgement.

    Why it matters: An early hands-on account of a Mythos-class model that shows how multi-hour autonomous agent runs shift the human role from steering to commissioning outcomes.

June 9Tuesday
  1. MiniMax Blog58

    MiniMax details MaxProof: generative-verifier RL and evolutionary search for mathematical proof

    MiniMax published a technical blog explaining MaxProof, the framework behind M3 exceeding the human gold-medal threshold on IMO 2025 and USAMO 2026. The pipeline trains three expert models for proof generation, error judgment and proof repair, then runs an evolutionary test-time search that samples 32 candidates, verifies each 4 times and refines over up to 10 rounds. MiniMax says the key lesson is that a generative verifier used as an RL reward should prioritize low false positives over average accuracy, citing M2-cycle reward hacking where the false-positive rate rose from about 2.9% to 8.0% during training.

June 1Monday
  1. MiniMax Blog87

    MiniMax M3 released with 1M context, frontier coding and native multimodality

    MiniMax released M3, an open-weight model it says is the first to combine frontier coding and agentic performance, a 1M-token context window, and native image and video input. M3 uses MiniMax's new MSA sparse attention architecture, which the company reports cuts per-token compute at 1M context to 1/20 of the previous generation and speeds prefill by more than 9x and decoding by more than 15x. It scores 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1 and 74.2% on MCP Atlas, and is available through MiniMax Code, the Token Plan and the API, with weights and a technical report promised within 10 days.

    Why it matters: MiniMax M3 combines a 1M-token context, frontier coding and agentic scores, and native image and video input in one open-weight model, with MSA cutting per-token compute at long context.

  2. Modal Blog46

    Modal: Reinforcement Learning for LLM Post-Training Is an Infrastructure Problem

    Modal says the bottleneck in RL post-training of LLMs is now infrastructure, not algorithms, and has released an open-source library drawn from running RL at scale. It details three pain points — glue code, cluster queueing and GPU under-utilization — and reports RDMA weight sync cutting Qwen3 8B update time from 2.62s to 41ms.

May 29Friday
  1. Every22

    Compound Engineering Expands From Four Steps to Eight

    Compound engineering, the AI-native engineering philosophy, has expanded its core loop from four steps to eight, adding Ideate and Brainstorm at the front and Polish at the end. The original loop—brainstorm, work, review, compound—remains the core engineering cycle, but the update reflects where human attention now goes: deciding what to build and judging whether the finished product feels right.

May 27Wednesday
  1. MiniMax Blog72

    MiniMax upgrades Agent as Mavis and launches Agent Teams for long-running tasks

    MiniMax upgraded MiniMax Agent under the new name Mavis and launched Agent Teams, which let multiple role-specific Agents run in parallel on the desktop for long, complex tasks. The system uses Leader, Worker and Verifier roles with a state-machine Team Engine that cycles tasks through producing, verifying and done, and it merges TokenPlan and Agent Plan into one subscription spanning CLI, API and Agent across M2.7, music, video and voice. MiniMax says the Agent will be open-sourced soon, expected alongside the MiniMax M3 model, while the desktop app is already available.

    Why it matters: MiniMax explains the design tradeoffs behind its Leader-Worker-Verifier Agent Team, including handoff, sharing and aggregation costs, offering a concrete reference for building multi-Agent runtimes.

  2. Modal Blog38

    Modal opens Role-Based Access Control to all Team and Enterprise users

    Modal is making Role-Based Access Control (RBAC) available to all users on Team and Enterprise plans, built around Environments as enforceable security boundaries. Restricted Environments make workspace members read-only by default, with only explicitly granted users or agents able to deploy and manage resources, and RBAC ties into Audit Logs for a full record of who did what and when.

May 21Thursday
  1. Vapi Blog20

    Vapi: Bringing Voice AI to ACCESS Chronic Care Panels

    Vapi argues that voice AI can now make voice the economically viable channel for ACCESS chronic disease management, citing Hiya data that 42% of consumers prefer voice for healthcare communication, up from 33% the prior year. The post details three production workflows, including Spring Venture Group's Medicare lead qualification agent, built by four engineers in one week, which lifted transfer-to-close rate by 3% and delivered over $5.8M in combined savings and new revenue.

  2. Modal Blog58

    Modal raises $355M Series C at a $4.65B valuation

    Modal raised $355 million at a $4.65B post-money valuation in a round led by General Catalyst and Redpoint, with Menlo, Bain Capital Ventures and Accel joining as new investors. The company says it has grown fivefold since September and surpassed $300 million in annualized revenue, with more than 1 billion sandboxes launched on the platform and sandboxes now driving over a third of revenue. Modal plans to expand low-latency inference, close the training and inference loop for reinforcement learning, and add granular RBAC so customers can give agents capabilities without risk.

May 20Wednesday
May 19Tuesday
  1. Modal Blog55

    Modal launches first-class Sandboxes integration with Claude Managed Agents

    Modal announced a first-class integration between Modal Sandboxes and Anthropic's Claude Managed Agents, letting all tool calls run in a self-hosted Modal Sandbox while Anthropic's managed agent loop handles session state. Modal cites fast cold starts on custom images, snapshot and volume persistence, burst pricing with no idle costs, short-lived connect tokens, and upwards of 100,000 concurrent sandboxes per customer. The company also published two reference examples, a CLI agent and a Slack bot, on the same Modal plus Managed Agents backbone.

May 15Friday
  1. Every40

    Every Is Redesigning Its Plus One AI Agents as Shared Team Resources

    Every is changing its Plus One product so its AI agents work as shared team resources with defined jobs, rather than one personal assistant per employee. The company says its first iteration, built on OpenClaw, brought more frustration than efficiency, with agents often failing to follow directions. The next version is meant to be more stable, with skills and integrations still part of the plan.

May 12Tuesday
  1. Modal Blog58

    How Modal achieved truly serverless GPUs

    Modal published an engineering account of how it cut AI inference replica spin-up from roughly 2,000 seconds to about 50 seconds using four components: a buffer of idle healthy GPUs, a lazy content-addressed filesystem called ImageFS, host-side checkpoint/restore via gVisor's runsc, and CUDA checkpoint/restore built on Nvidia driver support. Across over ten thousand cold starts, mean vLLM boot latency fell from 95,679 ms to 13,797 ms and SGLang from 83,713 ms to 17,486 ms for a ~1 GiB Qwen 3 0.6B model in bf16. The post also notes limits, including single-GPU-only snapshots and host-environment sensitivity that requires multiple snapshots per deployment.

  2. Vapi Blog50

    Vapi Raises $50M Series B as Its Voice Agents Pass 1 Billion Calls

    Vapi announced a $50M Series B led by Peak XV, with YC, M12, Kleiner Perkins and earlier investors participating, bringing total funding to $72M. The round follows a year in which Vapi's enterprise revenue grew 10x and its voice agents crossed 1,000,000,000 calls handled. Vapi says Amazon Ring moved from evaluation to running 100% of inbound calls on its platform in two weeks, with CSAT rising.

May 11Monday
May 8Friday
  1. Berkeley AI Research38

    Adaptive Parallel Reasoning: A Survey of Efficient Inference Scaling

    A Berkeley AI Research survey examines adaptive parallel reasoning, in which a reasoning model itself decides when to decompose subtasks, how many concurrent threads to spawn, and how to coordinate them. The post contrasts this with fixed parallelism methods such as self-consistency, Best-of-N, Tree of Thoughts, MCTS, ParaThinker, GroupThink and Hogwild! Inference, which impose the parallel structure externally. Sequential reasoning scales linearly in tokens and latency and risks context-rot, motivating parallel threads that explore independently and concurrently.

May 5Tuesday
May 4Monday
May 1Friday
  1. Vapi Blog34

    Open Stack vs Integrated Solution: Key Considerations for Voice AI Builders

    Vapi Blog interviews Cartesia's Iz Shalom on choosing between integrated and open modular voice AI stacks. Integrated stacks suit narrow use cases and fast proofs of concept, while open stacks fit multiple use cases, multilingual support, and swapping components. The piece urges teams to measure P99 latency in their own environment and build tight evaluations before production.