A new paper from the AI as Normal Technology team ran a shadow evaluation in which frontier AI agents were asked to answer the main research questions of two unpublished AI papers, given thousands of dollars of API credits and compute plus six days of wall-clock time, and the original authors unambiguously rejected both agent papers.
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.
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.
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.
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.
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.
Ethan Mollick published an opinionated guide arguing that using AI now means running agentic systems that combine a model with tools, not just chatting with a chatbot. He recommends ChatGPT (Work and Codex modes) or Claude (Cowork and Code modes) at $20/month for real work, starting with GPT-5.6 Sol or Fable/Opus at High thinking levels, and keeping approval prompts on to guard against prompt injection. He also says Google currently has no leading frontier model or Codex/Code equivalent, though Gemini Notebook and Gemini Omni remain useful for research and video editing.
Vapi launched Model Intelligence, a bundle of features that helps builders pick model combinations for voice agents. It includes four Model Presets (Balanced, High Intelligence, Ultra Fast, Cost Saver) bundling a transcriber, LLM and voice, plus per-model cost, latency and quality metrics drawn from over 1 billion Vapi calls.
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.
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.
A Princeton University researcher used an ICML keynote to argue that the "AI as Normal Technology" framework holds unless a discontinuity such as recursive self-improvement arrives, and that no lab milestone will suddenly eliminate all jobs. The talk urges building complementary skills, agency and judgment rather than racing to accumulate wealth before skills become irrelevant.
Kieran Klaassen describes "polish," the final step of his compound engineering workflow, where he runs the app himself and tells the agent what feels off until it doesn't. Using the /ce-polish command from the compound engineering plugin, he checks out a branch, starts the dev server, and opens the running app beside the agent in his editor. He cites a Cora email card animation that passed all automated checks and review agents but still slid in from the top of the screen instead of opening from the click point.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A journalist and communications expert argues that the best stories and ideas live in people as tacit knowledge, and AI models have not yet replicated how a skilled human questioner draws them out. She calls these people "Socrates-as-a-service" and warns that without a way to surface such knowledge, brands will sound like everyone else's content.
Vapi's team reviewed thousands of voice deployments and identified five characteristics that predict whether a voice agent use case will succeed: high call volume, repetitive and predictable conversations, clear success criteria, and two more. Missing one trait can be compensated for, but missing three means exploring alternative use cases.
OpenAI published a realtime prompting guide for voice agents, covering Realtime 2 reasoning, preambles, tools, and exact entity capture. The guide explains how to structure prompts so realtime voice agents handle reasoning and tool use while capturing entities precisely.
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.
Spiral general manager Marcus Moretti runs a full product solo using Claude Code, which he says has eliminated the drudgery of product management by moving work from 10 different apps into a single chat thread. The only product document he wrote himself was the roadmap, drafted over six versions; every PRD and ticket has been written by Claude.
OpenAI released a Developers plugin that gives ChatGPT and Codex access to the OpenAI API Platform and API setup guidance, with adaptations for Claude Code and Cursor bundling portable developer skills and the public OpenAI Docs MCP server. It covers API key setup, Agents SDK app building, API migration to the Responses API, and troubleshooting common API failures.
Factory built an open-source benchmark testing 13 models on 50 real pull requests from Sentry, Grafana, Keycloak, Discourse and Cal.com, with each model reviewing every PR at least three times at high reasoning effort and an LLM judge scoring findings against a human-curated golden set. GPT-5.2 led at 60.5% F1 for $1.25 per PR, ahead of Claude Opus 4.6 at 59.8% F1 and $3.11 per PR, while MiniMax M2.7 reached 45.6% F1 at $0.15 per PR and Kimi K2.5 hit 51.9% F1 at $0.41. Factory says cost explains only about 21% of quality variance, and it open-sourced the golden set, evaluation scripts and a Droid Action GitHub Action supporting all 13 models.
Factory released an Automated QA skill that drives apps the way a real user would, filling forms, typing into terminals and calling endpoints, then posts a structured report with screenshots, terminal snapshots and API traces as a single comment on each pull request. The skill runs on every push via Droid, can be configured as an optional CI check triggered by a PR label, comment command or manual dispatch, and is also available locally through /qa in any Droid session. Setup uses /install-qa, and the feature is available today in all Factory plans.
OpenAI published an API deployment checklist that walks developers through moving workloads onto the Responses API and the GPT-6 model family, including gpt-6-astra, gpt-6.1-sol, gpt-6 Sol and gpt-6 Luna. The guide covers reasoning.effort levels from low to max, text.verbosity, tool_search with deferred tool loading, Programmatic Tool Calling, Multi-agent, compaction, prompt caching and WebSocket mode, which OpenAI says has shown up to roughly 40% faster end-to-end execution for workflows with 20 or more tool calls. It also details reliability and safety steps such as handling 429 and 503 errors, background=True for long jobs, safety_identifier and misalignment monitoring.
Why it matters: OpenAI's official deployment checklist consolidates model selection, reasoning effort, tool search, compaction and WebSocket mode into one migration guide for GPT-6 workloads.