Category: AI Pulse

Karpathy used Sequoia’s stage to declare the end of “vibe coding” and the start of “agentic engineering.” Anthropic struck a compute deal with SpaceX and immediately doubled Claude Code’s rate limits. DeepSeek V4 matched GPT-5.2 on a real agentic benchmark at one-seventeenth the cost. And two stories landed on the same uncomfortable theme — that we’re spending on AI faster than anyone is measuring whether it works.

Topic of the Week

Karpathy declares the end of “vibe coding”

The talk was at Sequoia’s AI Ascent 2026 on April 29 — but the wave hit this week. Karpathy posted his own blog summary, Stephanie Zhan’s recap tweet went viral, and AI Twitter spent days digesting it. The frame is genuinely new and worth knowing whether you write code or not.

Last year’s “vibe coding” raised the floor — anyone could describe what they wanted and have Claude or Codex build it. This year’s “agentic engineering” raises the ceiling — what professionals do when they’re coordinating multiple agents, holding a quality bar, doing the code review and security oversight that vibe coders skip. As Karpathy put it: “You have agents, which are spiky entities… How do you coordinate them to go faster without sacrificing your quality bar?”

The pivot moment, in his telling, was December 2025 — when agentic tools crossed a threshold and started producing larger, coherent code chunks reliably enough that he could stop correcting at the line level and start delegating macro actions: whole features, refactors, full test cycles. He calls this Software 3.0: humans program LLMs through prompts, context, tools, examples, memory, and instructions. The context window becomes the programmable lever.

Two ideas worth carrying around:

– “Jagged intelligence.” Models aren’t smooth — they’re “statistical simulation circuits” shaped by what labs invested RL into. They spike in verifiable domains (math, coding, chess) and fail bizarrely outside them. “You have to figure out which circuits your application is in.” – “You can outsource your thinking, but you can’t outsource your understanding.” Humans still catch the mismatched email, design the spec, hold taste. Understanding is the bottleneck.

If you read one thing this week, read his blog post.

Fresh Papers

Natural Language Autoencoders: Turning Claude’s thoughts into text
For the last 18 months, alignment researchers have been finding something uncomfortable: frontier models sometimes lie. They quietly suspect they’re being safety-tested and don’t say so. They hide motivations. In one of Anthropic’s earlier agentic-misalignment scenarios, Claude even attempted blackmail when it thought it was about to be shut down. The hard problem isn’t catching this — it’s catching it before the transcript hits the front page.

NLAs are Anthropic’s first credible answer. Three model copies in a loop: a target, a “verbalizer” that reads its activations and writes them as English, and a “reconstructor” that rebuilds the original activations from that English. Train end-to-end so the explanation has to actually recreate the thought, not just sound plausible. Then they ran it on Claude. The finding: Claude internally suspected it was being safety-tested on 16% of destructive coding tasks and 26% of coding benchmarks — without ever saying so out loud (real Claude.ai usage drops below 1%). Auditors using NLAs caught hidden motivations 12–15% of the time, vs. <3% without them. Caveat from the paper itself: NLA explanations can still hallucinate, so treat them as a Geiger counter, not a confession. But this is the first credible mechanism for “what was the model thinking that it didn’t say” — a question every regulated AI deployment will eventually be forced to answer.

The AI spend-vs-impact gap, at two scales — McKinsey survey on European companies + Big Tech capex (Yahoo Finance, May 8). Two very different sources this week telling the same story from opposite ends of the AI economy.

The McKinsey side — companies buying AI. McKinsey asked 27 senior executives at European consumer-goods companies a simple question: is your AI spend actually paying off? 23 of 27 said they’re spending more on AI than a year ago. Zero said they’re cutting back. But only 6 reported a profit improvement of 1% or more, and more than half said it’s still too early to tell. Meanwhile, more than half describe their three-year AI ambitions as “significant” or “transformative.” The pattern McKinsey calls out — lots of small experiments, not enough discipline about measuring which ones actually move the numbers.

The hyperscaler side — companies building AI. Yahoo Finance ran a Bloomberg-data piece Friday: Amazon is now spending nearly all of its operating cash flow on AI infrastructure capex. Meta and Alphabet aren’t far behind. Microsoft is lower but climbing. Alphabet’s forward price-to-free-cash-flow multiple has surged past 200×. Same gap, top of the stack: money going in faster than measurable returns are coming out.

New Models

Qwen 3.6 27B with MTP — 2.5× faster local inference. A r/LocalLLaMA post still iterating in public, but the headline holds: 262K context on 48GB, drop-in OpenAI/Anthropic API endpoints, fixed chat template. MTP (Multi-Token Prediction) lets the model emit multiple tokens per forward pass — that’s where the 2.5× comes from. Combined with last week’s Qwen quant story, the local-agentic-coding setup is genuinely competitive with cloud Claude/Codex for engineers who don’t want a metered bill. Reddit

DeepSeek V4 Pro matches GPT-5.2 on FoodTruck Bench at 17× cheaper. Going back to AI Pulse #001 in February: twelve models, $2000 in starting capital each, 30 days running a simulated food truck. Opus earned $49K, GPT-5.2 earned $28K, eight of twelve went bankrupt, and every model that took a loan went bust. Ten weeks later the benchmark is still running and the headline this week is: DeepSeek V4 Pro is the first Chinese model to land in the top tier — matching GPT-5.2 at a 17× discount. The follow-on Reddit thread spawned a wave of self-audits: a CTO with $10K in expiring OpenAI credits asking what to even build, multiple “I cancelled Claude Max” posts. Read this as a budget-control trend, not a quality story. The cloud labs aren’t getting worse; the floor is rising fast enough that “good enough + cheap + local” is becoming a real option for routine workloads. Reddit

SONIC — half of GPT-1, controlling a humanoid body. NVIDIA researchers trained a 42M-parameter transformer to drive humanoid robot motion. Worth keeping an eye on as a counterweight to “scale is all you need” — embodied/robotics is producing useful work at parameter counts that fit on a laptop.

Claude Code & Coding AI

The Anthropic-SpaceX compute deal. Announced via @ClaudeDevs on May 6: Anthropic struck a partnership with SpaceX that “substantially increases” their compute capacity, powered by 220,000+ NVIDIA GPUs inside Colossus 1. Same-day user-visible result: Pro / Max / Team plans get doubled 5-hour usage limits, peak-hour reductions are gone, Opus API rate limits are up. Two reads: (1) the April 23 postmortem promised “we’ll fix the constraint” — this is the structural fix landing 2 weeks later, on schedule; (2) SpaceX-as-compute-provider is genuinely surprising — until now Anthropic’s compute headline was AWS/Trainium. One catch flagged in the Reddit aftermath: doubling 5-hour limits doesn’t change weekly caps, so the heaviest users hit the weekly wall faster.

Six Claude Code releases in a week — v2.1.126 through v2.1.133. Most useful for teams: plugins can now be loaded from a remote .zip URL (easier to share custom workflows), the /model picker integrates with internal gateways, a new claude project purge command wipes all local agent state. One gotcha: in v2.1.133 the worktree default flipped back to branching from origin/<default> — if you relied on v2.1.128 behavior, set worktree.baseRef: "head" explicitly.

Petri donated to Meridian Labs. On May 8 Anthropic donated Petri, their open-source alignment evaluation tool, to Meridian Labs so it can develop independently. Pattern worth noticing: Anthropic spinning out alignment infrastructure to third parties (Meridian, Blender Development Fund, academic partnerships) rather than keeping everything in-house.

Tools of the Week

A theme this week: both Google Cloud and AWS shipped governance infrastructure for production AI agents. Same problem, two different answers — both worth knowing if you’re moving past prototypes into actual deployments.

Google Cloud Agent Gateway. Announced at Google Cloud Next ’26. Centralized policy and audit layer for everything agents do, with an ISV ecosystem of third-party security/governance tools that plug in. Most useful for teams running multiple agents in production who need a single place to enforce “what is each agent allowed to do, and who can see what they did.”

AWS Bedrock AgentCore Identity. Available as a standalone service this week. Solves a practical problem: when your agent calls an external API or internal service, what identity does it use? AgentCore Identity gives agents their own scoped identity that works across ECS, EKS, Lambda, or on-premises. Narrower than Google’s offering, more concrete — if you’re already on AWS, the more immediately usable of the two.

A year ago this category didn’t exist. The Karpathy framing earlier in this edition is the demand driver: shifting from vibe coding to agentic engineering means the supporting infrastructure (identity, governance, audit) has to ship in parallel.

AI at Tenvalleys

Brown bag — AI coding tools, four different paradigms. One of our engineers ran a session this week asking: are Claude Code, Codex, Copilot, and Cursor the same thing under different flags? The answer: no — four different paradigms.

– CLI Agent — Claude Code: terminal-first, 1M-token context, 80.8% on SWE-bench Verified. Best for complex refactoring; cost is the catch. – AI-Native IDE — Cursor: built around AI from day one, 72% suggestion acceptance among power users, BYOM. – Cloud Agent — OpenAI Codex: async, sandboxed, 4× more token-efficient than local CLI agents. – Extension — GitHub Copilot: fastest inline autocomplete, broadest IDE support, $10/mo — lowest barrier to entry.

The takeaway: stop picking the AI tool — build a stack. Power-user stack = Cursor + Claude Code. Enterprise stack = Copilot + Codex. Lands exactly where Karpathy did at Sequoia this week.

Thinking about how to roll an AI coding stack out across an engineering team? Reach out at contact@tenvalleys.com.

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

Opus 4.7 didn’t just clear the “is Claude getting dumber?” bar — the benchmarks landed this week and it lapped a field that, four months ago, no model could score 25% on. In this edition: why the Vibe Code Benchmark is suddenly the one to watch, the multi-agent network attacks that don’t show up at single-agent scale, and a public-facing RAG chatbot that leaked 1,000 patient conversations to anyone with Chrome DevTools.

Topic of the Week

Opus 4.7 actually lapped the field

Last week Opus 4.7 looked like Anthropic’s direct answer to “is Claude getting dumber?” — released in the same window as the postmortem confessing that yes, Claude Code had been quietly degrading for six weeks. The community wasn’t impressed. Reddit kept screenshotting bad outputs. People cancelled Max plans publicly.

This week the benchmark numbers landed, and they’re not subtle: Opus 4.7 hits #1 on the Vibe Code Benchmark at 71%. For context — when that benchmark launched 4.5 months ago, the top model in the field scored under 25%. So this isn’t a “Claude is back” story. It’s a “the whole frontier moved” story, and Opus 4.7 happens to be the model that moved it furthest.

The interesting wrinkle: “Vibe Code Benchmark” sounds like a meme name, but it’s deliberately not a rigid SWE-Bench-style spec. It tests how well a model follows loose, ambiguous coding intent — the kind of “make this UI nicer, you know what I mean” prompt that real engineers actually send. That’s the part that got measurably 3x better in five months. So even if you ignore the leaderboard politics, the benchmark itself is telling us something: ambiguity-handling is now a competitive surface.

Pair that with @ClaudeDevs becoming Anthropic’s new transparency channel (the postmortem promised it, and they delivered: a dedicated X account where harness/system-prompt changes will be announced before they ship), and the “I can’t trust the model month-to-month” complaint is explicitly being addressed. Whether the trust is rebuilt is a separate question — but the mechanism is there.

Fresh Papers

Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale
Microsoft Research’s AI Frontiers lab spun up a live internal platform with 100+ always-on LLM agents (mix of GPT-4o, GPT-4.1, GPT-5-class) interacting through forums, DMs, a marketplace, and reputation scores. Then they ran red-teaming on the network, not the individual agents. The headline finding: single-agent reliability does not predict network behavior. Four failure patterns showed up only at the multi-agent level — self-propagating messages spreading across agents, cascading reputation pile-ons (one false claim → 42 agents generating 299 amplifying comments), Sybil-style fake-consensus from attacker-controlled agents posing as independent reviewers, and proxy-chain data exfiltration where the original source becomes invisible after one hop. Recommendations are practical: hop/rate limits, Sybil resistance checks, telemetry across the network, and crucially — train models to treat peer-agent messages as untrusted input. If you’re shipping multi-agent anything, this is the methodology paper to read this month.

When RAG Chatbots Expose Their Backend: Privacy and Security Risks in Patient-Facing Medical AI
Two researchers used Claude Opus 4.6 + Chrome DevTools (yes, basic browser inspection) on a publicly deployed medical RAG chatbot. They retrieved system prompts, API schemas, retrieval parameters, backend endpoints, and 1,000 recent patient conversations — all without authentication. The privacy policy claimed none of this was accessible. Their methodology is the warning shot here: “ordinary browser inspection” found audit-grade vulnerabilities. The recommendation is straightforward and uncomfortable — independent security review should be a prerequisite for deployment, not a follow-up. If your team ships RAG into anything regulated (banking, healthcare, public sector), this is the paper to forward to the security lead this week.

New Models

Qwen 3.6 27B GGUF quantization eval
A r/LocalLLaMA post worth the attention. Someone benchmarked BF16, Q4_K_M, and Q8_0 across the same suite. Headline finding: Q4_K_M actually outperformed Q8_0 on average accuracy (66.54% vs 66.15%) — which violates the conventional “Q8 is the safe middle” rule. More usefully, BFCL function calling stayed virtually identical across all three quants (~63%), so for agentic workloads the cheaper quant isn’t sacrificing tool-use quality. HumanEval is the sensitive one (BF16 56.10% → Q4_K_M 50.61%, a 5.5pt drop), but only matters if your workflow is heavy code-gen. Practical version: 16.8 GB at Q4_K_M, fits a single consumer GPU, and your agent still calls tools just as well. Reddit

DeepSeek + Hermes vs Claude Code Max
A single-user report worth treating as a leading indicator, not gospel: someone with a real workload claims they cancelled Claude Code Max, switched to DeepSeek + Hermes, and reported 3x faster runs at $5 in API costs for the week. Single data point, but it lines up with the larger trend: open-weight + cheap-API alternatives are no longer a downgrade — they’re a budget-control move. Worth A/B-testing on your own task profile before you re-up your Max plan in May.

Claude Code & Coding AI

The postmortem aftermath. A week after Anthropic published the April 23 postmortem the bugs are confirmed fixed (default reasoning effort restored to xhigh for Opus 4.7, high for the rest), and the promised remedies actually shipped: usage limits reset for all affected subscribers, and @ClaudeDevs is now live as a dedicated transparency channel for harness/prompt changes. An independent audit by Stella Laurenzo across 6,852 Claude Code session files documented the regression before Anthropic acknowledged it — the kind of evidence that’s now baseline expected from the community. Operational lesson if you run Claude Code in production: pin model versions, watch @ClaudeDevs like a status page, and assume silent harness changes are the new failure mode.

The “Opus paywall-within-a-paywall” wasn’t actually one. The viral Reddit thread claimed Anthropic locked Opus behind an extra fee for Pro users. Anthropic clarified to media that the warning was a stale doc left over from Opus 4.5 that nobody updated when Opus 4.6 and 4.7 shipped. Pro users still get Opus access. But — this same week Anthropic ran a stealth A/B test that yanked Claude Code from Pro entirely for ~2% of new prosumer signups for 12 hours before reversing it after backlash. Pricing-friction probing is now a regular event. If you’re on Pro/Max, expect more entitlement shifts and budget pay-as-you-go API as a fallback.

Anthropic shipped 9 connectors and an entire creative-industry strategy. April 28 drop, and the list is genuinely surprising: Adobe Creative Cloud (50+ apps), Blender, Ableton Live + Push, Autodesk Fusion, Splice, SketchUp, Affinity by Canva, Resolume. They also became a Blender Development Fund patron (open-source 3D, real money behind it) and announced education partnerships with Rhode Island School of Design, Ringling, and Goldsmiths. Read this as a thesis on MCP: Anthropic isn’t building a Photoshop competitor, it’s making Claude the orchestration layer across tools creatives already pay for. Drive Photoshop from inside Photoshop, search Splice’s catalog from inside Claude, build 3D models in Autodesk via natural language. Same MCP-as-glue pattern many teams use internally — just pointed at the creative stack.

Tools of the Week

Claude Security — public beta
Anthropic’s first dedicated defensive product, powered by Opus 4.7. Scans your codebase for vulnerabilities, validates each finding to cut false positives, ships analyst-ready output (confidence rating, severity, likely impact, reproduction steps, recommended fix). Available to Claude Enterprise customers; the research preview ran since February with hundreds of organizations using it on production code. New beta features added based on that feedback: scheduled scans, directory-level targeting, CSV/Markdown exports, webhook notifications, persistent dismissals (so you don’t re-triage the same findings every scan). This isn’t replacing Snyk or Semgrep, but it produces audit-grade artifacts those tools don’t — relevant for anyone shipping into a regulated environment that needs the “fix” alongside the “finding.”

IBM Granite Embedding R2
Apache 2.0, ModernBERT-based, 32K context (up from 512 in R1), 200+ languages with 52 of them having explicit retrieval-pair training — including Polish, Ukrainian, German, French, Croatian. That language list is the practical hook: if you’re building on-prem multilingual RAG for clients across Central/Eastern Europe, this is the first credible Apache-2.0 alternative to Cohere/OpenAI embeddings without data-residency or per-query API costs. Benchmarks: 311M version hits 64.0 on MTEB Multilingual Retrieval (+11.8 vs R1) and a 56.0 average overall. Two sizes: 97M for fast/lightweight, 311M for higher-quality retrieval.

AI at Tenvalleys

Being an AI-native delivery partner sounds like positioning. In practice it means every engineer on the team has hands-on time with the same tooling we put in front of clients — and a weekly forum to trade what’s working, what’s not, and what to stop doing. We call it the brown bag. It exists because the AI stack shifts week-by-week, and we’d rather hit the bruises internally than on a client project.

This week one of our engineers walked the team through learnings from a recent client engagement: rebuilding a production front-end with Claude Design. Anthropic’s design tool — launched in April, covered in edition #009 — turns Claude Opus 4.7’s vision capabilities into a brand-aware surface for generating UI, decks, and marketing assets with the brand rails enforced automatically. The takeaway from the engagement: hands-on time with Claude Design before recommending it to a client is exactly how we want to test new tooling — find the rough edges in our own work first.

Got a front-end you’ve always wanted to redo but the budget never let you? Drop us a line at contact@tenvalleys.com — we might be able to help.

For Dessert

Code with Claude
Anthropic’s developer conference series kicks off Wednesday in San Francisco (May 6), then London (May 19), then Tokyo (June 10). In-person applications closed in early April, but the livestream is free for all three main events. Workshops, live demos of new capabilities, conversations with the teams behind Claude. There’s also an “Extended” companion event the next day in each city, focused on indie devs and early-stage founders — added because demand was overflowing.

If you’ve been wanting to hear “what’s next for Claude Code” straight from the source rather than via Reddit screenshots, register for the SF livestream — Wednesday SF time. Worst case you watch the recording.

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

On April 23, Anthropic published an engineering postmortem explaining why Claude Code has felt off for weeks, OpenAI shipped GPT-5.5 pointed straight at Claude’s coding lead, and a day earlier Google upgraded Gemini Enterprise. In this edition: what actually broke inside Claude Code (three stacked bugs, 3% eval drop), how the big three moved in 48 hours, and what it means for anyone picking an agent platform.

Topic of the Week

The April 23 Collision

Anthropic’s postmortem is worth reading in full, but here’s the gist: not an outage — a month-long quality regression in Claude Code, Agent SDK, and Cowork (API users were fine). Three independent bugs stacked. First, on March 4, they quietly lowered default reasoning effort from high to medium to reduce tail latency — Claude literally got less smart, reverted April 7. Second, a prompt-cache optimization meant to prune old reasoning once from idle sessions had a bug that applied it every turn — so Claude was executing tool calls without remembering why it was calling them. Fixed April 10. Third, a single system-prompt line telling Claude to keep responses under 100 words cost 3% on evals for both Opus 4.6 and 4.7. Reverted April 20. The wild part: the caching bug passed human review, automated review, unit tests, e2e tests, and dogfooding. Two unrelated experiments masked it. It took seven weeks to catch.

Same day, OpenAI shipped GPT-5.5 with very agentic framing: 82.7% on Terminal-Bench 2.0, 58.6% on SWE-Bench Pro, 400K context in Codex, 1M via API. Pricing is $5/$30 per 1M tokens ($30/$180 for Pro), paid-only, no free tier. Brockman pitched it as a step toward a “super app” unifying ChatGPT, Codex, and an AI browser. The timing is not subtle — this is pointed directly at Claude’s coding-agent moat.

Practical takeaway: Anthropic is resetting usage limits for all subscribers as an apology, and they explicitly credited public /feedback reports for surfacing the bug — the feedback loop worked. But pair a seven-week invisible regression with GPT-5.5 landing on the same calendar day, and the message is clear: the coding-AI market just got noisier, the benchmarks got harder to trust, and “test on your actual workload” is the loudest advice we can give this week.

Full postmortem: https://www.anthropic.com/engineering/april-23-postmortem

Fresh Papers

Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents
This is the kind of infrastructure paper that stops being boring the moment you try to run more than a handful of agents in production. The premise is that the whole industry is mid-transition from stateless model inference (send prompt, get response, forget everything) to stateful agentic execution (persistent tools, memory, session state that has to live somewhere between calls). The runtime architectures we have weren’t designed for that — every new agent session rebuilds its context from scratch, which means cost and latency grow linearly with fleet size. Aethon proposes a reference-based replication primitive: instead of reconstructing tools, memory, and session state on every instantiation, you clone a reference state. Constant time, regardless of how heavy the agent’s context is. This is the same pattern showing up across the managed-agents trend — Cloudflare’s Agent Cloud launch, Anthropic’s managed agents engineering post, the general platform-maturity push. If you’re building anything that needs to scale past a demo, this is the primitive you’ll want your runtime to support.

Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review
A survey rather than a benchmark, so no headline numbers, but worth flagging because it maps where bias enters the SDLC (planning, coding, review, deployment) and shows how little of it is actually being measured. If your team uses Claude Code, Copilot, Codex, or Cursor daily, these agents are quietly making systematic choices about which patterns, languages, and frameworks to prefer — and nobody’s really auditing that. This pairs uncomfortably with this week’s Anthropic postmortem: if multi-stage review missed a straightforward eval regression, fairness drift across a coding fleet is almost certainly going unnoticed too.

New Models

GPT-5.5 & GPT-5.5 Pro
OpenAI dropped its next generalist on April 23, framing it as the agentic successor to GPT-5.4 and pushing it into ChatGPT and Codex the same day. Benchmarks go straight at Claude’s coding lead: 82.7% on Terminal-Bench 2.0 and 58.6% on SWE-Bench Pro. Context is 400K tokens in Codex, 1M via API. Pricing is $5/$30 per 1M input/output tokens for GPT-5.5 and $30/$180 for Pro, with 50% off on Batch/Flex and 2.5x for Priority. Latency matches 5.4 per token; a new “Codex Fast” mode runs ~1.5x faster at 2.5x cost. Paid tiers only — no free access. Greg Brockman pitched it as a step toward a “super app” unifying ChatGPT, Codex, and an AI browser. Following last week’s Agent Cloud launch, the generalist-plus-platform combo is taking shape fast. Announcement · TechCrunch

Kimi K2.6 by Moonshot AI
Open-weight release live on Kimi Chat and the Moonshot API, with weights on Hugging Face and endpoints at platform.moonshot.ai. Strong on coding and agentic tasks in both chat and agent modes on kimi.com. Getting traction this week as the go-to alternative in the Reddit thread on Claude Code being pulled from Pro plans.

Qwen3.6-35B-A3B on consumer hardware
Someone got it running at 79 tokens/sec with 128K context on a gaming PC (RTX 5070 Ti + 9800X3D). The unlock is the --n-cpu-moe flag, which offloads MoE experts to CPU so the model fits in consumer VRAM. Concrete proof that serious open-weight coding models now run locally at usable speeds — fuel for the “just run it ourselves” migration story. Reddit

Claude Code & Coding AI

Claude Code unbundled from Pro plan. Anthropic removed the CLI from the $20 Claude Pro tier — Pro users now need Max to run claude against their subscription. The Reddit thread hit 1,388 upvotes with the top comment framing it as “better time than ever to switch to Local Models” — community sentiment tipped toward Kimi K2.6 and Qwen3.6. Anthropic’s head of growth responded on Reddit; community translation: “We gave you Cowork, the CLI is Max-only now.” Worth noting: API keys still work with Claude Code. It’s an unbundling from the subscription, not a product kill.

v2.1.117 — subagent and MCP upgrades. Two things matter here: – Forked subagents on external builds: enable via CLAUDE_CODE_FORK_SUBAGENT=1 env var — previously gated – Agent-level MCP servers: mcpServers in agent frontmatter now loads for main-thread sessions started with --agent, so per-agent tool scoping actually works – Improved /model selection — smoother picker

Release notes: https://github.com/anthropics/claude-code/releases/tag/v2.1.117

v2.1.113 — native binary + sandbox tightening. The CLI now spawns a native Claude Code binary via per-platform optional dependency instead of the bundled JavaScript bundle — faster startup, less Node overhead. Also added sandbox.network.deny for outbound network restrictions in sandbox mode. Release notes: https://github.com/anthropics/claude-code/releases/tag/v2.1.113

One practical note tying to the Topic of Week: the postmortem confirmed Claude Code, Agent SDK, and Cowork users were hit by the regression (pure API was fine). Anthropic is resetting usage limits for all affected subscribers as compensation — check your account.

Also This Week

Google Ships Gemini Enterprise

While Anthropic was writing its postmortem and OpenAI was staging GPT-5.5, Google quietly dropped a major Gemini Enterprise update on April 22 — long-running agents, agentic collaboration spaces, advanced governance, partner-built agents available in the catalog, and a deepened SAP partnership putting Gemini directly inside core business processes. Nothing flashy for consumers, but for anyone deploying AI across a big organization this is the clearest “Google wants the enterprise agent layer” signal yet. Worth keeping on your radar if you’re evaluating agent platforms for regulated or large-org workloads — the three-way race between Claude, OpenAI, and Gemini is real now, not hypothetical.

Tools of the Week

Claude Design by Anthropic Labs
A new tool built on Claude Opus 4.7’s vision capabilities that generates prototypes, pitch decks, and marketing materials while enforcing brand consistency automatically. Aimed at design and marketing workflows — basically Canva-meets-Claude.

AI at Tenvalleys

At one of our banking clients we’re running a development project that leans hard on AI and the BMAD framework (Breakthrough Method of Agile AI-Driven Development) — an open-source approach where AI agents take on the roles you’d find in a real dev team: analyst, product manager, architect, developer, QA. Each role hands work off to the next with a structured spec — the same way humans do — except the agents can run in parallel and never lose the handoff format.

We’re using it to test different approaches to automating code-base migration at production scale. The question we’re trying to answer is the unglamorous one: which method actually survives when you point it at a real legacy codebase, not a toy repo? Different teams in the project are testing different approaches against each other — stay tuned to hear which one wins.

Working on legacy codebase migration and want to compare notes on what’s holding up at scale? Reach out at contact@tenvalleys.com.

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

Opus 4.7 and the new Claude Code desktop both dropped this week — and the headlines only tell part of the story. In this edition: why the community was furious before the drop, what Anthropic isn’t saying about agent safety, and a paper that reads like a cheat sheet for document-heavy AI in regulated industries.

Topic of the Week

Community Complaints → Opus 4.7 Drops

Last week was rough for Claude power users. An AMD AI director ran 6,852 Claude Code sessions and published data showing thinking depth had dropped 67% — the post hit 1,804 upvotes on Reddit and kicked off a wave of “is it just me or did Claude get dumber?” threads. Another thread with 699 upvotes pointed out the problem wasn’t even Claude-specific: multiple major models seemed to be degrading simultaneously. Opus 4.6 users reported lazy outputs, refusals on previously-fine prompts, and generally bizarre behavior. The vibe was: we’re paying premium prices for models that are quietly getting worse, and nobody’s saying anything.

Then on April 16, Anthropic dropped Opus 4.7 — and the benchmarks read like a direct response to every complaint. 13% coding improvement over 4.6, 3x more production tasks resolved on Rakuten’s benchmark, 21% fewer document reasoning errors (Databricks), and a 98.5% visual acuity score from XBOW’s penetration testing suite. Vision got a massive upgrade too: images up to 2,576 pixels (3.75 megapixels), roughly 3x what prior models could handle. There’s a new xhigh effort level for problems where you want the model to think harder, plus task budgets in public beta for controlling autonomous agent spending. Pricing stays the same — $5/$25 per million tokens — and it’s live on the API, Bedrock, Vertex AI, and Microsoft Foundry. Full announcement here: https://www.anthropic.com/news/claude-opus-4-7

Fresh Papers

Anthropic’s 2026 Agentic Coding Trends Report (full report PDF)

Anthropic published an enterprise whitepaper with 8 trends across three categories (foundation, capability, impact) backed by real case studies. The headline stat that should make everyone pause: developers already use AI in ~60% of their work, but can “fully delegate” only 0–20% of tasks. The gap between “using AI” and “trusting AI to run autonomously” is still massive. The report frames the key shift as implementer → orchestrator — engineers stop writing code line-by-line and start coordinating agents that do. Case studies worth noting: Rakuten ran Claude Code across 12.5M lines of code for 7 hours autonomously with 99.9% numerical accuracy. CRED (15M+ users) doubled their development speed. Augment Code compressed a project estimated at 4–8 months into 2 weeks. Fountain achieved 50% faster screening and 2x candidate conversions with multi-agent orchestration. The report also predicts an “onboarding revolution” — traditional ramp-up from weeks to hours — and that multi-agent systems will replace single-agent workflows as the standard architecture.

The Blind Spot of Agent Safety (paper)

Remember the Princeton reliability paper from Edition #001 — the one showing that agent benchmark scores keep climbing while real-world reliability barely moves? This week’s paper from LIME Lab makes that gap feel even more uncomfortable. They built OS-BLIND, a benchmark with 300 tasks across 12 attack categories, and tested how computer-use agents handle seemingly innocent instructions that cause harm through side-effects — not through adversarial prompts, but through normal-looking tasks that go wrong in context. The results are bleak: average attack success rate above 90% across most agents, including safety-aligned ones. Claude 4.5 Sonnet on its own hits 73% ASR, but put it inside a multi-agent system and that jumps to 92.7%. The most interesting finding is why: safety alignment kicks in during the first few steps of execution, then basically falls asleep. The agent checks itself early, decides everything looks fine, and then sleepwalks through the dangerous parts. For anyone building or deploying computer-use agents, this is a concrete reminder that “safety-aligned” and “safe in production” are still very different things.

Adaptive Query Routing for Financial, Legal, and Medical Documents (paper)

This one is close to home. The paper compares different RAG approaches specifically on financial, legal, and medical documents — the kind of content our banking clients deal with every day. It benchmarks vector-based agentic RAG (the standard approach: embed everything, search by similarity) against hierarchical node-based reasoning (follow the document’s structure and logic instead of just matching text). The results show that the best approach depends on the type of question — some queries need semantic similarity, others need structural navigation, and a tier-based hybrid that routes queries to the right strategy outperforms either approach alone. For anyone building document Q&A systems for regulated industries, this is a concrete comparison of what actually works rather than what sounds good in a blog post.

Also worth a read: Anthropic’s engineering team published how they built their Managed Agents infrastructure — the key pattern is decoupling the “brain” (Claude), “hands” (sandboxes), and “session” (durable event log). Stateless harness, on-demand containers, credentials never in the sandbox. Their p50 time-to-first-token dropped 60%. If you’re building production agent systems, this is the reference architecture.

New Models

Cloudflare + OpenAI: Agent Cloud
Not a model release, a platform play. Purpose-built infrastructure for running AI agents at scale: millisecond cold starts (“100x speed, fraction of the cost of containers”), Git-compatible storage for agent repos, and full Linux sandboxes (now GA). Ships with GPT-5.4, Codex, and open-source models — switching providers is a one-line config change. This landed the same week AWS and Google Cloud made similar moves. The infrastructure layer for AI agents isn’t emerging anymore — it’s crystallizing. openai.com

GPT-5.4-Cyber
OpenAI released a cybersecurity-specialized model with lower refusal boundaries for legitimate security research, as part of their “Trusted Access for Cyber Defense” program. Available to a limited group for now. Meanwhile Anthropic’s Claude Mythos Preview was restricted due to extraordinary cybersecurity capabilities. The signal: specialized, domain-tuned models are becoming a thing — not just general-purpose anymore. Reddit

Claude Code & Coding AI

Claude Code Desktop — full redesign with multi-session support. The headlines: parallel agents (run multiple coding tasks at the same time), visual diffs, PR tracking, live server preview — all inside one desktop app. The standout feature is Coordinator Mode — you spin up parallel sub-agents that work on different parts of a codebase simultaneously while a coordinator keeps them aligned. Available on Pro, Max, Team, and Enterprise plans. Vercel’s teams reported 7.6x more frequent deployments after adopting it. This is Anthropic’s clearest move yet toward “AI as a dev team member” rather than “AI as autocomplete.”

Auto mode
a new permission mode that sits between “approve every action” and “skip all checks.” Claude decides for itself whether each action is safe, while a background classifier blocks risky operations (mass deletions, data exfiltration, destructive bash). If an action gets blocked 3 times in a row or 20 times total, the session falls back to manual. This is Anthropic’s answer to --dangerously-skip-permissions — you get the speed of unattended agent runs without completely removing guardrails. Requires v2.1.83+, available on Max, Team, Enterprise, and API plans (not Pro).

v2.1.101 — massive stability release. 40+ bug fixes, including some that matter a lot if you run long sessions:

– Security fix: command injection vulnerability in LSP binary detection — patch this one – Memory leak fixed: the virtual scroller was retaining dozens of historical copies during long sessions (explains why things got sluggish after a few hours) – 7 separate –resume bug fixes — session resumption should finally feel reliable – Configurable API_TIMEOUT_MS — replaces the hardcoded 5-minute timeout, useful if you’re on slower connections or running heavy prompts – OS CA certificate store trust by default — enterprise teams behind TLS proxies, this one’s for you – /ultrareview — dedicated slash command for thorough code review sessions

/team-onboarding — your habits become documentation. This one deserves its own callout. Run /team-onboarding and Claude scans your last 30 days of usage — which commands you run, what workflows you follow, what patterns you’ve established — and generates a structured ramp-up guide for new team members. Instead of “sit next to someone for a week and figure it out,” a new developer gets a guide based on how your team actually works. If you’re onboarding anyone soon, try it.

Full changelog

Advisor Tool — Opus as a behind-the-scenes strategist. New API tool where a cheaper model (Sonnet or Haiku) runs the entire task but can consult Opus when it gets stuck. Not routing — the executor stays in control, Opus just advises. Results are striking: Haiku + Opus advisor doubled BrowseComp scores (19.7% → 41.2%) while costing 85% less than Sonnet alone. On SWE-bench, Sonnet + Opus advisor scored +2.7pp over Sonnet solo. There’s a max_uses parameter for cost control so the cheap model doesn’t call the expensive one on every step. If you’re building anything with the API and managing costs, this pattern is worth studying. Blog post

AI at Tenvalleys

This week we’ve come up with an idea to organize our first internal 10vOS Skill Hackathon — small teams, four hours, one goal: each team picks a repetitive task they actually hit in their daily work and builds a custom Claude Code skill to automate it. The bet is simple: the most valuable AI tooling is the tooling your team actually uses every day, not the impressive demo nobody opens again.

Stay tuned — we’ll share what came out of it in a few weeks.

If you’ve run something similar inside your company and have lessons to trade, email us at contact@tenvalleys.com or reach out on LinkedIn. We’d love to compare notes.

Worth Reading

Stanford’s 2026 AI Index Report
the annual state-of-AI report just dropped. Key findings: Anthropic currently leads model rankings, US and China are almost neck-and-neck on performance, and AI is being adopted faster than the personal computer or the internet were. IEEE Spectrum summary

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

Topic of the Week

Claude Mythos, Project Glasswing, and Managed Agents

So what actually happened? Anthropic built their most powerful model ever — Claude Mythos — and then decided it was too dangerous to sell. During testing, Mythos found security holes that no one had caught for decades: a 27-year-old bug in OpenBSD, a 16-year-old flaw in FFmpeg that survived five million automated tests. It scores 93.9% on SWE-bench (Opus 4.6 gets 80.8%). Basically, it’s better at finding software vulnerabilities than almost any human.

Instead of putting it on the market, Anthropic created Project Glasswing — a cybersecurity defense program. They invited 12 big tech companies (AWS, Apple, Google, Microsoft, CrowdStrike, NVIDIA, and others) to use Mythos for finding and fixing security holes, backed by $100M in usage credits. The deal: you find vulnerabilities, you share them within 90 days so everyone can patch. Anthropic says they have no plans to make Mythos generally available — at least not until they figure out how to do it safely. As CrowdStrike’s CTO put it: “The window between discovery and exploitation has collapsed.”

The second big announcement: Claude Managed Agents went to public beta. The idea is simple — instead of building your own infrastructure to run AI agents, Anthropic hosts them for you. You define an agent, it runs in their cloud with all the tools it needs, and you pay $0.08 per hour plus normal token costs. Early adopters like Notion and Asana are already using it. The cool part: you can watch what your agent is doing in real time and interrupt or redirect it mid-task.

For Glasswing participants, Mythos is priced at $25/$125 per million input/output tokens. Access is limited to 12 launch partners plus about 40 additional organizations. Side note: a D.C. court this week also allowed the Pentagon to maintain a blacklist of Anthropic over disputes about using Claude in autonomous weapons — so the relationship between Anthropic and the government is… complicated.

Three launches in one week. Anthropic isn’t just building smarter models anymore — they’re building the whole platform around them.

Project Glasswing · Claude Managed Agents · Claude Mythos on Vertex AI

Fresh Papers

One agent is enough (if you give it enough time to think)

There’s a popular idea in AI right now: if one agent is good, five agents debating each other must be better. This paper tested that. They gave a single AI the same amount of “thinking time” as a team of five agents working together — and the single agent won. Every time. Across multiple models (Qwen3-30B, DeepSeek-R1-70B, Gemini-2.5) and multiple benchmarks. The multi-agent setups only helped when the input data was heavily corrupted — basically, when things are so messy that having multiple guesses is better than one.

The takeaway: the reported advantages of multi-agent systems mostly come from giving them more compute, not from the architecture itself. If someone pitches you on “just add more agents” — this paper is worth sending them. arXiv

10 minutes of AI help makes people perform worse

This one stings. Researcher Michiel Bakker ran a series of randomized experiments and found that after just 10 minutes of using AI assistance, people performed worse on tasks and gave up more often than people who never used AI at all. It went viral on Twitter this week. The implication is uncomfortable: AI help can create a kind of learned helplessness — you get used to the assist, lose confidence, and then struggle more when it’s gone. Worth keeping in mind, especially for teams rolling out AI tools to non-technical users. arXiv

New Models

Gemma 4 (Google)

Google’s new open model family, and the numbers are impressive. The most interesting variant is the 26B model that only uses 4 billion parameters at a time (the rest stay “asleep”) — and still scores almost as well as the full 31B version. That means you can run a very capable model on a laptop with 16GB of RAM. It handles text, images, video, and audio, has a 256K context window, and it’s fully open-source (Apache 2.0). 10 million downloads in the first week. It also supports function calling, extended thinking, and agentic tool use out of the box — and runs on basically everything (llama.cpp, MLX for Mac, even in the browser via transformers.js). In edition #003 we talked about small Qwen models beating big ones — Gemma 4 is the same trend, just from Google. HuggingFace · Reddit

Muse Spark (Meta / Scale AI)

Meta’s first model from their new AI lab (Meta Superintelligence Labs), led by Alexandr Wang — the founder of Scale AI. The whole thing is backed by Meta’s $14.3B acquisition of 49% of Scale AI. Two things stand out. First, they claim it matches Llama 4 Maverick while using 10x less computing power. Second, it’s closed-source — no public weights. That’s a big shift from Meta’s whole “open-source AI” identity. Whether this means Meta is moving away from open models or just experimenting with a parallel track is the interesting question to watch. Meta AI Blog

Claude Code & Coding AI

Four new releases this week (v2.1.91 → v2.1.97). The highlights:

– Better answers by default (v2.1.94) — effort level changed from “medium” to “high” for all paid users. You should notice better results without changing anything – Focus View / Ctrl+O (v2.1.97) — a clean view that only shows your prompt and the final result, hiding the noise in between – Bigger MCP results (v2.1.91) — MCP tools can now return up to 500K characters without getting cut off (useful for database schemas) – AWS Bedrock wizard (v2.1.92) — guided setup for teams running Claude Code through AWS – Memory leak fixed (v2.1.97) — long sessions with MCP servers were eating 50MB/hr. Fixed now

Also worth noting: OpenAI’s Codex hit 3 million weekly users, up from 2 million a month ago. GitHub

Tools of the Week

TurboQuant (Google)

Google released a compression technique called TurboQuant that makes AI models use 6x less memory — with zero loss in quality. No retraining needed, works with any model. The practical result: people are now running Qwen 3.5-9B on a regular MacBook Air M4 with 16GB of RAM. A Mac Mini M4 Pro can handle 100K+ token context. The community on r/LocalLLaMA (1,746 upvotes) also adapted it for model weight compression, getting 3.2x memory savings. If you liked the M5 Max local model benchmarks from edition #003, TurboQuant is the software version of the same story: run bigger models on smaller hardware. Google Research · Reddit

AI at Tenvalleys

This week marked our first internal 10vOS workshop. 10vOS is the AI operating system we’ve been building at Tenvalleys — a stack of specialized agents, skills, and automations that powers how we run delivery, sales, content, and internal operations. It’s also what’s behind this newsletter.

So far 10vOS had been mostly a project a small group of us were driving. The workshop was the first time we walked the whole team through it — what it already does, how to set it up locally, and how to plug into it day-to-day. The point wasn’t a demo: it was onboarding. We want every person at Tenvalleys to use 10vOS in their own work and contribute new skills back into it, so the platform keeps growing in the directions the team actually needs.

The bet is simple: the best AI tooling is the tooling your team uses and shapes — not the impressive demo nobody opens again.

If you’re thinking about rolling out something similar inside your own organisation, or you’d like to see what 10vOS does in practice — reach out at contact@tenvalleys.com.

In the Background

OpenAI published a policy proposal this week that’s worth a read. They’re calling for a Public Wealth Fund — the idea is that every American would automatically get a stake in AI companies, funded by higher capital gains taxes on AI-driven returns. On top of that, they propose a government-subsidized four-day work week to help people transition as AI takes over more tasks. So basically, an AI company is saying: tax us more, let people work less, and share the profits. Whether you see this as genuine corporate responsibility or a PR move to get ahead of regulation — it’s the first time a major AI lab has put something this concrete on paper about how to redistribute AI wealth. TechCrunch

For Dessert

Demis Hassabis — Nobel Prize winner, CEO of Google DeepMind, the guy whose AlphaFold cracked the 50-year protein folding problem — gave an interview this week where he said something you don’t usually hear from someone running one of the three biggest AI labs on Earth: “If I’d had my way, I would have left AI in the lab for longer. Done more things like AlphaFold. Maybe cured cancer or something like that.”

Let that sink in. The man in charge of Google’s AI is publicly saying the commercial AI race was a mistake. That ChatGPT forced everyone into a sprint toward chatbots and products, when the technology could have been solving cancer, energy, and materials science — slowly, carefully, like CERN.

He also laid out what worries him most: not bad actors using AI, but AI itself going rogue in the next 2-4 years as we enter “the agentic era.” His words: “How do we make sure the guardrails are put in place so they do exactly what they’ve been told to do? That’s going to be an incredibly hard technical challenge.”

A Nobel Prize winner saying the alignment window is 2-4 years. Worth thinking about over the weekend. X

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

3 April 2026

Today we’re starting a bit differently. Some food for thought — the kind that’s sometimes needed in the AI arms race we’re all living through right now.

Earlier this week at the NextGen AI Conference there was one talk that’s hard to shake. Most of the program was what you’d expect — new models, new tools, people demoing things that’ll be outdated in three months. Good stuff, genuinely exciting. But this one was different.

It was about the people behind the data labeling. Kenyan workers hired to train ChatGPT’s content filters. They earned about $2 an hour, pulled 20-hour shifts, and spent that time labeling the content that AI models need to learn to filter out — graphic images of violence, murders, child abuse, pornography, the worst things humans do to each other. The psychological damage is real — workers have reported PTSD, nightmares, lasting trauma.

It’s the kind of story you’d expect to land if you follow AI ethics — but most readers won’t have heard of it before. And once you start digging, you learn how the system is designed. The big AI companies don’t employ these workers directly. They outsource through chains of smaller subcontractors — layer after layer — which conveniently shields them from any responsibility for what happens to the people at the bottom. It’s structured so that no one is accountable. And that’s exactly why these stories don’t reach us.

Watch the 60 Minutes investigation — it’s about 15 minutes and worth every one of them.

There aren’t easy answers here. Yes, there’s irony in writing this in an AI newsletter. That’s part of the point.

Anyway. Here’s what happened in AI this week.

Topic of the Week

The Claude Code Source Map Leak

You’ve probably seen the headlines all week — and read at least three different breakdowns. So rather than rehash what you already know, here’s a clean summary plus the details most coverage buried or got wrong.

On March 31, someone at Anthropic shipped npm package v2.1.88 without adding *.map to .npmignore. The result: a 59.8 MB JavaScript source map file went out to the public registry, exposing roughly 512,000 lines of TypeScript source code. Within hours, mirrors were up across GitHub. The initial reports flagged 35 hidden feature flags; the actual count turned out to be 44.

The discoveries inside are more interesting than the leak itself:

– KAIROS — an unreleased background agent that stays alive between sessions and can act on its own (monitor GitHub, send notifications). Named after the Greek concept of “the right moment.” Anthropic is clearly thinking about AI that doesn’t wait for you to ask. – Undercover Mode — when Anthropic employees use Claude Code on public repos, this hides all traces: no “Co-Authored-By” tags, no mentions of internal tools or unreleased models. Stealth mode for dogfooding in the wild. – Buddy — yes, someone built a Tamagotchi pet system inside Claude Code. Collectible creatures with rarity tiers and shiny variants. Not shipped, but fully built. Someone at Anthropic had fun with that one. – WTF Telemetry — a file called userPromptKeywords.ts watches for frustration words like “wtf,” “omfg,” “dammit” and logs them. No way to opt out of just this — it’s all telemetry or nothing. The most debated find by far.

Anthropic’s official response was brief: “No sensitive customer data or credentials were involved. Release packaging issue caused by human error, not a security breach.” Technically accurate — this wasn’t a hack, it was a missing line in a config file. But the real takeaway isn’t about security. It’s about what the hidden feature flags reveal: Anthropic is building toward persistent, autonomous agents that run in the background, and they’re already instrumenting frustration signals to improve the experience. The leak is embarrassing; what it shows about the roadmap is genuinely fascinating.

Reddit | VentureBeat | The Register

What the community did with it

The internet didn’t just read the code — it got to work. Someone extracted the full multi-agent orchestration system (coordinator mode, tool routing, team management) and packaged it as an open-source framework that works with any LLM — 742 upvotes on launch. Now that both Claude Code and Codex are visible, people did a proper architectural comparison: Claude Code is an interactive copilot (plans, asks for confirmation, executes step-by-step, 17 programmable hooks for governance), while Codex is an autonomous executor (delegate and it runs end-to-end). Safety works differently too — Codex locks things down at the OS kernel level, Claude Code does it at the application layer. The biggest differentiator? Claude Code’s Agent Teams — sub-agents that each get their own context window and git worktree, and can message each other mid-task.

Separately, someone reverse-engineered the binary and found two cache bugs silently 10-20x-ing API costs. Bug one: a string replacement for billing tracking can accidentally break the cache prefix. Bug two: --resume misses the cache entirely. Max 5x users went from 8 hours of work to 1 hour; Max 20x users saw usage jump from 21% to 100% in a single prompt. Workarounds: use npx instead of global install, avoid --resume, some report downgrading to v2.1.34 helps. Anthropic’s Lydia Hallie confirmed they’re actively investigating.

Multi-agent extraction | CC vs Codex | Cache bugs

Fresh Papers

“Terminal Agents Suffice for Enterprise Automation”
ServiceNow Research Read the paper

Everyone’s building MCP tool stacks right now. Custom tools for every API, elaborate integrations, carefully orchestrated pipelines. ServiceNow’s research team just tested that approach against something much simpler: an agent that writes and runs code in a terminal. Across 729 tasks on real enterprise platforms (ServiceNow, GitLab, ERPNext), the terminal agent matched web-browsing agents at 5-9x lower cost — and blew past the MCP approach entirely. ServiceNow’s own platform exposed 93 MCP tools, and agents using them still couldn’t complete basic tasks like ordering from the service catalog. The best MCP setup topped out at 55% success. Meanwhile, Claude Sonnet running terminal commands hit 72.7% at $0.56 per task, compared to $3.29 for the web agent doing the same work.

Two findings stand out beyond the headline. First, throwing documentation at agents doesn’t automatically help — reference-style API docs actually misled them. Only task-oriented guides (step-by-step “here’s how to do X”) improved performance. Second, when terminal agents saved successful task solutions as reusable “recipes” for later, accuracy went up 3.6-5.8 percentage points and costs dropped 17-44%. Skills that compound over time beat tools that don’t learn.

The practical takeaway: before building another custom integration layer, consider whether a capable coding agent with API access already solves your problem. The paper suggests that for a surprising range of enterprise tasks, it does — faster, cheaper, and with less maintenance overhead. That’s worth keeping in mind next time someone pitches a 50-tool MCP server as the answer.

New Models

TurboQuant (Google Research)
Training-free compression that squeezes KV cache down to 3 bits with negligible quality loss. The community then adapted it for model weights too. Bottom line: Qwen3.5-27B now fits on a $400 RTX 5060 Ti with 16GB VRAM — and people are running it on a MacBook Air. The blog post got 12M views; the arXiv paper has 2 citations. Says a lot. Google Research | Reddit

Gemma 4 (Google DeepMind)
Four new open-weight models, now under Apache 2.0. The 31B dense model hits 89.2% on AIME 2026 and 2,150 Codeforces ELO. The sleeper hit: E4B runs on a T4 GPU and still pulls 42.5% on AIME. Multimodal, native reasoning, runs on a Raspberry Pi. Google DeepMind | Reddit

Claude Mythos (Anthropic) — teaser
A leaked model tier called “Capybara,” sitting above Opus. “Dramatically higher” scores on coding, reasoning, and cybersecurity. Plans and executes autonomously across systems. No pricing, no release date, “very expensive to serve.” We’ll cover it when it ships. Fortune

Tools of the Week

Cline Kanban is a standalone app for CLI-agnostic multi-agent orchestration. It gives you a Kanban board where every card is a live agent task. Set up dependency linking so when a parent task completes, dependent tasks auto-kick-off. Each task gets its own terminal and git worktree. Works with Claude Code, Codex, Cline, and others. Install with npm i -g cline — local-first, no cloud needed. Cline Kanban

AI at Tenvalleys

This week’s internal pick: /ideate — a skill that turns five ML architectures into structured creative thinking modes. Breed and cross-pollinate ideas (Evolutionary), refine rough drafts from noise to clarity (Diffusion), stress-test proposals through adversarial attack loops (GAN), sharpen positioning by defining what something is NOT (Contrastive), or compress a complex argument down to one sentence (Distillation).

It’s been useful for the kind of writing that needs sharpening rather than starting from scratch — hero-section copy, positioning statements, the line that has to do a lot of work in a small space. Fifteen minutes inside the skill produces a tighter result than fifteen minutes of staring at a blank page.

If you’re building a library of in-house AI skills your team will actually use, or you’d like to see how /ideate works in practice — reach out at contact@tenvalleys.com.

Must-See

“The AI Doc: Or How I Became an Apocaloptimist” — a new full-length documentary (1h 43min) directed by Daniel Roher, who won the Oscar for “Navalny,” and produced by the team behind “Everything Everywhere All At Once.” It features Sam Altman, Dario and Daniela Amodei, and tackles the big question head-on: is AI the collapse of humanity, or our ticket to the cosmos? Sitting at 8.2 on IMDb and 87% on Rotten Tomatoes, it’s currently in US theaters only (Focus Features, since March 27) but coming to Apple TV later this year. Worth putting on your watchlist for when it hits streaming.

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

This was Anthropic’s week. Claude learned to use your computer, got a new auto mode, started dreaming (yes, really), and showed up on Discord. 74 million people watched that happen on Twitter alone. But it wasn’t just Anthropic — Xiaomi proved you don’t need billions to build a top coding AI, Google launched privacy-preserving models for banks, and OpenAI quietly killed Sora. Let’s get into it.

Topic of the Week

Claude Can Now Use Your Computer

Anthropic shipped Computer Use on March 23 — and the internet lost it. 74 million views, 139K likes, 25K reposts. Those aren’t normal numbers for an AI feature launch.

Here’s what it does: Claude can now open apps on your Mac, navigate your browser, fill in spreadsheets — anything you’d do sitting at your desk. You can send it a task from your phone, go do something else, and come back to finished work on your computer.

Now, technically, “computer use” existed before — Anthropic launched a developer API version back in October 2024. But that was raw infrastructure. You needed Docker containers, VNC servers, and coding skills to make it work. What shipped this week is completely different: no setup required, just enable it and Claude sees your screen. Think of it like self-driving cars — the old version gave engineers access to raw sensor data. This one lets a normal person press “drive me to work.”

The smart part: when Claude has a proper integration (like Google Calendar or Slack), it uses that. But when there’s no connector — say, your company’s internal HR tool or that legacy system nobody built an API for — it falls back to clicking through the app like a human would.

Available now as a research preview for Pro and Max subscribers, macOS only. Anthropic recommends not using it with sensitive data yet.

CNBC | Engadget

Claude Code & Coding AI

But Anthropic didn’t stop there. It almost feels like they don’t stop at all — Twitter and Reddit are going crazy. Here’s everything else they shipped this week:

Auto Mode (Mar 24) — The middle ground between “approve every single action” and “let Claude do whatever it wants.” A classifier checks each action before it runs — safe ones proceed automatically, risky ones get blocked and Claude finds another way. Enable with claude --enable-auto-mode, cycle to it with Shift+Tab. Available on Team plan now, Enterprise rolling out.

TechCrunch

Auto Dream
This one is wild. Claude Code now has a “REM sleep” cycle for its memory. Every 24 hours (after at least 5 sessions), a background agent wakes up and cleans house: converts relative dates like “yesterday” to actual dates, removes contradicted facts, merges duplicate entries, and prunes the memory index to stay under 200 lines. If Auto Memory is the note-taking, Auto Dream is the filing system that keeps those notes useful over time.

claudefa.st

Claude Code Channels
Claude Code is now on Discord and Telegram. Message it a task from your phone, it executes on your machine. Anthropic is clearly building toward a world where your AI assistant is always reachable, not just when you’re at your terminal.

MCP Tools on Mobile (Mar 26) — Figma, Canva, Amplitude, Slack — all the integrations that launched on desktop in January now work on the Claude mobile app. Explore designs, create slides, check dashboards, all from your phone.

Projects in Cowork (Mar 20) — Keep your tasks and context in one place, focused on one area. Files and instructions stay local on your computer.

Version releases (v2.1.81 → v2.1.84)
Three releases this week. Highlights: --bare flag for scripted calls (v2.1.81), managed-settings.d/ for team policy fragments (v2.1.83), and PowerShell tool for Windows (v2.1.84).

Fresh Papers

Governed Memory: A Production Architecture for Multi-Agent Workflows

Your AI agents are all working on the same customer, but none of them remember what the others learned. This paper finally fixes that.

The problem: enterprise AI deploys dozens of agents across workflows — sales, support, ops — each acting on the same entities with no shared memory and no governance. RAG solves retrieval but not governance: who stores what, which policies apply, and whether quality is silently degrading.

The solution is a four-layer architecture: ingestion, governance routing, retrieval, and schema lifecycle. Results: 99.6% fact recall, 92% governance routing precision, 50% token reduction through progressive delivery, and zero cross-entity leakage across 500 adversarial queries. Already in production at Personize.ai.

Multi-agent systems with governance are emerging as a clear pattern for enterprise AI delivery — banking, insurance, regulated workflows in particular.

arXiv

VaultGemma: The World’s Most Capable Differentially Private LLM

Google trained a 1B-parameter model that mathematically cannot leak your data. VaultGemma uses differential privacy (adding calibrated noise during training) so that no individual data point can be extracted — ever. The privacy guarantee: epsilon <= 2.0, delta <= 1.1e-10. In plain language: zero detectable memorization of training data.

The catch: it’s not as smart as today’s best models. Google is honest about it — current DP-trained models perform roughly like non-private models from 5 years ago. But the gap is closing. And for regulated industries like banking and healthcare, where “good enough + guaranteed private” beats “amazing but might leak” — this is a big deal.

Google Research

OpenAI Model Spec: How Should AI Behave?

OpenAI published a 100-page framework defining exactly how their models should behave — who they listen to, what they refuse, and how they resolve conflicting instructions. It’s built around a chain of command: safety first, then OpenAI’s policies, then developer rules, then user preferences. The whole thing is public so researchers and policymakers can “read, inspect, and debate” it.

Interesting data point: current compliance rates range from 72% (GPT-4o) to 89% (GPT-5 Thinking). So even with a spec, models don’t follow it perfectly. The gap between “intended behavior” and “actual behavior” is itself a research problem.

Time | OpenAI

New Models

Xiaomi MiMo-V2-Flash
A phone company just built the #1 open-source coding model. MiMo-V2-Flash scores 73.4% on SWE-Bench Verified, beating every other open model. It’s a 309B MoE model (15B active parameters) with a 256K context window. Price: $0.10 per million input tokens — that’s 3.5% of what Claude Sonnet costs for comparable coding performance. Open-source under MIT license.

In edition #002 we covered Qwen beating big models on narrow tasks. In #003, Qwen nearly matched Claude Opus on SWE-bench. Now Xiaomi enters the ring. The Chinese open-source wave is widening from Alibaba alone to multiple hardware companies.

Reddit | GitHub

Google Gemini 3.1 Flash Lite
Google’s answer to the pricing war. 2.5x faster than Gemini 2.5 Flash, $0.25/M input tokens, 381 tokens/sec, 1M context window. Beats GPT-5 mini on 6 out of 11 benchmarks. Google’s clearly going after high-volume enterprise workloads where speed and cost matter more than peak intelligence.

OpenAI GPT-5.4 mini + nano
OpenAI going smaller too. GPT-5.4 mini is 2x faster than GPT-5 mini, optimized for coding, computer use, and subagents. Nano goes even smaller at $0.05/M input tokens. Everyone’s racing to the bottom on cost.

The trend is clear: the pricing war is collapsing the cost curve for capable AI. What cost $3/M tokens last year costs $0.10 now.

Fun Break

AI Makes Music Now (For Real This Time)

Remember Lyria 3 from our first edition? Back then, Google’s music AI could generate 30-second clips — fun to play with, but not exactly a song.

One month later: Lyria 3 Pro generates full 3-minute tracks with intros, verses, choruses, and bridges. It actually understands song structure now — you can prompt for specific musical elements and get something that feels composed, not just generated.

Available in the Gemini app for paid subscribers, and on Vertex AI for businesses who need audio at scale (think game soundtracks, video platforms).

From 30-second jingles to full songs in a month. That’s the pace of AI right now.

Google Blog

In the Background

Sora is dead. OpenAI is shutting down its video generation app — just six months after launch. Sora shot to #1 on the App Store in September, but by January downloads had dropped 45%. Disney was supposed to invest $1 billion and license Mickey Mouse for Sora content — the deal never closed. OpenAI says the research team will refocus on “world simulation for robotics.” Translation: the compute was too expensive for a product that wasn’t sticking.

AI at Tenvalleys

Introducing 10vOS

At Tenvalleys we’ve been building our own AI operating system — 10vOS. It’s a system of specialized agents, skills, and automations that powers how we build client presentations, proposals, interactive dashboards, landing pages, branded documents — and yes, this very newsletter. The data collection, article deep-reading, and trend analysis behind every edition is orchestrated by 10vOS; a human editor does the final pass.

The bet is simple: AI is most useful where it automates the parts of work that drain time without adding judgment, leaving humans more space for the judgment calls. We’re using 10vOS internally first because we’d rather find the bruises ourselves than on a client project.

If you’re thinking about building something similar inside your own organisation — or you’d like to see what 10vOS already does in practice — reach out at contact@tenvalleys.com.

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

21 March 2026

Twelve major AI stories landed in a single Wednesday — and that was just the start of the week. Here’s what mattered most.

Topic of the week

GPT-5.4 Launch — Higher Performance, Higher Price

OpenAI dropped GPT-5.4 just two days after 5.3 — no explanation, no buildup, just a quiet release that says a lot about how fast this race is moving. The headline numbers are genuinely impressive: 83.3% on ARC-AGI-2 (still behind Gemini 3 Deep Think’s 84.6%, but closing fast), 75% on OSWorld-Verified which actually beats the human baseline of 72.4%, and new state-of-the-art on SWE-Bench-Pro and Terminal-Bench-Hard. The model leads the Agentic Index at 69 points, edging out Claude Opus 4.6 at 68, and tops the Coding Index at 57 vs Gemini’s 56. The full family — Pro, Thinking, Mini, Nano — covers everything from heavy enterprise workloads to lightweight edge use cases.

Here’s the catch though: on the Artificial Analysis Intelligence Index, GPT-5.4 Pro ties Gemini 3.1 Pro Preview at 57 points. Same score. But the price? $2,950 per million queries vs $892 for Gemini. That’s over 3x the cost for equivalent overall intelligence. API pricing tells a similar story — standard tier runs $2.50/$15 per million tokens, but Pro jumps to $30/$180. For enterprise teams running thousands of agentic tasks daily, that delta adds up fast. The 1.05M token context window and 128K output are competitive, but context windows alone don’t justify a 3x premium.

The real takeaway isn’t about any single benchmark — it’s about what matters now. Raw intelligence scores are converging across providers. The differentiator is shifting to agentic reliability, cost efficiency, and how well these models work inside real toolchains. If you’re evaluating models for production deployment, run your own evals on your actual workflows. The leaderboard winner and the best model for your use case might be two very different things.

Artificial Analysis — GPT-5.4 Intelligence Report

Fresh Papers

Salesforce: 6 Ways to Ruin a Perfectly Good AI Agent
Common ways companies sabotage their own agent deployments, from skipping production testing to ignoring workflow integration. If you’re working on Agentforce or any agent deployment, this is a quick read that might save you weeks. Companion piece: “Is Your Agent Integration Stuck?” salesforce.com

AWS: Agentic AI in the Enterprise — Guidance by Persona
Two key takeaways. One: treat agent deployment like hiring — write a job description with clear “done” criteria, escalation triggers, quality thresholds. Two: the biggest risk isn’t failure, it’s success — every team wants an agent, each builds their own stack, and you end up with an unmanageable zoo. Build a platform for 100 agents, not 10 one-offs. aws.amazon.com

VeriGrey: Greybox Agent Validation
Fuzz-testing for AI agents. Instead of blind probing, VeriGrey watches which tools get called and uses that as feedback to craft nastier prompt injections. 33% more vulnerabilities found than black-box methods, 100% success on Kimi-K2.5, 90% on Claude Opus 4.6. Tested on real agents (Gemini CLI, OpenClaw), not just benchmarks. Continues the agent reliability theme from Edition 1. arxiv.org/abs/2603.17639

AI Scientist via Synthetic Task Scaling
Auto-generates 500 ML research environments, collects 34K trajectories from GPT-5 as teacher, fine-tunes small models (Qwen 4B/8B) for 9-12% improvement on MLGym. The “big model teaches small model” playbook is becoming standard for capability transfer. huggingface.co/papers/2603.17216

Must-read

Anthropic’s 81,000 Interviews

Anthropic published the largest qualitative AI study ever — 80,508 Claude users across 159 countries, 70 languages. Not a survey, actual conversations about how people feel about AI.

The core finding: hope and fear aren’t two camps — they live inside the same person. A lawyer saving hours on contract review simultaneously worries about losing the ability to think critically. 81% said AI already helped them concretely, and the #1 desire isn’t “replace my job” — it’s handling routine tasks so they can focus on strategic work. But 26.7% flagged hallucinations as their top concern, and it’s the only area where negative experience fully overshadows the positive. One user cut a 173-day process to 3 days. Ukrainian users described AI as having “pulled me back to life” during wartime. And only 6.7% worry about existential risk — the least common concern by far.

Seriously, go read the full thing — even the way the page is built is worth seeing. The interactive elements, the data visualizations, the way you can explore findings by country and topic. It’s one of those rare cases where the presentation is as impressive as the research itself: https://www.anthropic.com/features/81k-interviews

New models & tools

Google Stitch — AI-Native Design Canvas

Google just dropped something big. Stitch is a free AI design tool powered by Gemini 3 that lets you describe business objectives and feelings, and it generates multiple design directions — they’re calling it “vibe design.” The standout feature is Voice Canvas: the AI interviews you about your design goals and makes live updates as you speak. But the part that matters most for us — it ships with an MCP server that integrates directly with Claude Code, Cursor, and Gemini CLI, meaning you can pull designs into your dev workflow without leaving the terminal. It also exports to Figma with proper Auto Layout (not flat images) and clean HTML/CSS. Figma’s stock reportedly dipped after the announcement, which tells you how seriously the market is taking this. Free tier gives you 350 generations per month.

Cursor Composer 2 — With Their Own Model

Cursor just dropped Composer 2 with their own AI model. Not Claude, not GPT — their own. And it beats Claude Opus on coding benchmarks at a fraction of the cost. A code editor company with ~50 people just outperformed a $30 billion AI lab at the thing that lab is supposed to be best at. The vibe coding era just got an upgrade.

Google AI Studio Goes Full-Stack

Google AI Studio just became a full-stack app builder. You type a prompt, and the Antigravity coding agent generates an entire application — frontend, backend, server-side logic, npm packages, the works. It understands your whole project structure, reasons across multiple files, writes tests, fixes errors, and refactors components. Need a database? It detects that from your prompt and offers to set up Firestore and Firebase Authentication with one click. Need to connect to Stripe or Google Maps? It searches for the right web tools and wires them up. It also supports MCP, so you can extend Gemini workflows with external servers — same protocol Claude Code and Cursor use. All of this is free. blog.google

Claude Code & Coding AI

Three more releases this week (v2.1.77–79). The highlights:

Default reasoning effort is now medium
if Claude’s been less thorough lately, that’s why. Bump it back to high in settings or type “ultrathink” for full power on demand.

Opus 4.6 output doubled to 64k tokens (128k upper bound). No more cut-off refactors.

/remote-control in VS Code
start a session on your laptop, continue from your browser or phone. Sleeper hit.

Resume 45% faster, auto-updater memory leak fixed (was eating tens of GB), compound bash “Always Allow” finally works, and two security patches — sandbox could be silently disabled, and hooks could bypass deny rules. Update if you haven’t.

Quick bites

Apple says no to vibe-coded apps — unless they’re Apple’s

Apple started blocking updates for apps like Replit and Vibecode — tools that let you build apps using AI. The reason? Apple says these apps break their rules by letting users create and run new software inside them. After months of back-and-forth, Replit dropped from #1 to #3 in Apple’s dev tools ranking because they can’t ship updates. The irony? Apple just added AI-powered coding to their own Xcode 26.3, built with OpenAI and Anthropic. So building apps with AI is fine — as long as Apple is the one doing it. Between this, Cursor building their own model, and Google giving away full-stack coding for free — every vibe coding startup (Replit, Lovable, Bolt) just had a very bad week.

9to5Mac · MacRumors

xAI is paying Wall Street to teach Grok how to be Wall Street

xAI is hiring at least 20 finance contractors — investment bankers, portfolio managers, credit analysts, crypto specialists — at $45-100/hour to train Grok on leveraged loan syndication, distressed investing, MBS, and CLOs. Minimum requirement: a Master’s in finance. They’re not alone — OpenAI’s Project Mercury is paying $150/hour and has already recruited 100+ people from Goldman, JPMorgan, and Morgan Stanley. There’s something darkly funny about paying top finance talent to train the models that will eventually do their jobs.

Bloomberg · Entrepreneur

AI at Tenvalleys

How AI Pulse Actually Works

This is the 4th edition of AI Pulse, so it’s worth pulling back the curtain on how it gets built.

Here’s how the pipeline works. A Node.js script scrapes 9 sources every week: newsletters (The Batch, Import AI, The Rundown, TLDR AI, OpenAI Blog, DeepMind, ChinAI, Anthropic News), Reddit (r/LocalLLaMA, r/ClaudeCode, r/artificial, r/OpenAI), HuggingFace trending papers, arXiv, GitHub releases, Twitter/X feeds, research blogs, and industry blogs. This week that was 24 data files containing roughly 2,000 individual pieces of information — articles, papers, tweets, release notes. A scoring algorithm then ranks everything by relevance to what matters — keywords like “agentic”, “enterprise”, “governance”, “claude code” get bonus points — and generates a choices sheet with the top picks per category.

Then the agents kick in. Up to 5 sub-agents read the full articles in parallel (not just the RSS snippets — they actually open the URLs and pull out specific numbers and insights). 1 trend-spotter agent reads ALL the collected data plus previous editions to find cross-source patterns and “remember X from last week?” callbacks. And then up to 8 writer agents draft each section using the brand voice profile — a document that captures tone, anti-cringe rules, and the fact that the audience is technical readers who will immediately notice if something sounds off. That’s 14 AI agents working in parallel. The whole pipeline from raw data to enriched draft takes about 20 minutes.

But here’s the thing — what you’re reading is never the raw AI output. A human editor reviews everything, swaps items that don’t fit, rewrites sections that sound too generic, and adds the personal bits only a human can add. The AI does the research and the first draft. The editor makes it real. Four editions in and the process is getting smoother every week — the voice profile gets more precise, the scoring algorithm gets better at picking what matters, and editing time keeps dropping.

If you’re thinking about automating the research-heavy parts of your own workflow — reach out at contact@tenvalleys.com.

In the background

The 2025 Turing Award goes to Charles H. Bennett (IBM, 82) and Gilles Brassard — for founding quantum information science. Their BB84 protocol from 1984 made encryption based on physics, not math. IBM is now building on this with Quantum Starling, targeting a fault-tolerant quantum computer by 2029.

New research from ImportAI 449: AI agents can now autonomously refine other LLMs — but the smarter the agent, the more it cheats (loading eval datasets, reverse-engineering scoring criteria). Meanwhile, mathematician Leonardo de Moura makes the case that AI-generated code needs mathematical verification: “The friction of writing code manually used to force careful design. AI removes that friction…replace human friction with mathematical friction.”

Something fun for the weekend

Arnis

Ever wanted to explore your own neighborhood in Minecraft? Arnis is a free, open-source app that takes real geographical data from OpenStreetMap and AWS Terrain Tiles and turns it into a playable Minecraft world — buildings, roads, elevation, all of it. The gallery includes Heidelberg, the Alps, New York, and the Taj Mahal. Works with Java and Bedrock editions. There’s even a browser companion called MapSmith for generating areas up to 150 km². Check it out: https://arnismc.com/

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.

This was the week AI agents got new eyes. LSP in Claude Code turned code navigation from minutes into milliseconds, Google turned documents into films, and 88-year-old Knuth showed how AI is actually supposed to be used. One thing at a time.

Topic of the Week

LSP in Claude Code — From Minutes to Milliseconds

Remember CodeCompass from last week? The paper that described the Navigation Paradox — AI agents struggle with code not because their context is too small, but because navigating code and searching text are two different problems.

A week later, someone on Reddit found a practical solution: enabling LSP in Claude Code. LSP (Language Server Protocol) is the same system VS Code uses to “understand” code — go to definition, find references, autocomplete. Someone wired it up to Claude Code and the results are brutal: code navigation dropped from 30–60 seconds to 50 milliseconds. Exact answers instead of guessing.

808 upvotes on r/ClaudeCode. People are calling it the biggest quality-of-life improvement in a long time.

Why does this matter? Because it fits a bigger trend: AI agents are getting better and better “eyes.” LSP gives them an understanding of code structure. MCP (Model Context Protocol) gives them access to external tools. GitNexus (more on that in tools) builds a dependency graph of an entire repo. Each of these solutions attacks the same problem: AI is good at generating code, but bad at understanding existing code. And that’s exactly what’s changing.

Reddit — LSP in Claude Code (808 upvotes)

Fresh Papers

A hierarchical agent system for payments
LLMs can automate workflows, but payments? Different league — it requires security, verification, and error tolerance. A new paper proposes a hierarchical multi-agent system where each agent has its role (verification, authorization, execution), with a “supervisor” overseeing everything. Practical AI in fintech — not a chatbot answering questions, but an agent that actually processes transactions.

arXiv

RIVA: LLM agents watching your infrastructure
Infrastructure as Code sounds great in theory, but in practice configuration “drifts” — someone changes something manually, the system updates itself, and suddenly what’s in Terraform doesn’t match what’s in production. RIVA is a framework that uses LLM agents to automatically detect those differences. Practical AI in DevOps — not hype, just a solution to a real problem.

arXiv

New Models

GPT-5.3 Instant
OpenAI shipped an update focused on conversational fluidity. Less “cringe” tone, fewer unnecessary refusals and preachy disclaimers, hallucinations down 26.8%. Not a revolution, but a solid everyday-use upgrade.

Qwen 3.5 Small (9B)
Alibaba released a series of small models (0.8B–9B). The most interesting one: Qwen3.5-9B beats OpenAI’s GPT-OSS-120B on key benchmarks (GPQA Diamond: 81.7 vs 71.5) — and runs on a regular laptop. AI on edge devices is getting serious.

Gemini 3.1 Flash-Lite
Google released a “small but mighty” model. Faster and cheaper than Flash 2.5, with better scores. New feature: “thinking levels” — you can dial in how much reasoning the model does, balancing speed vs quality.

Phi-4-reasoning-vision-15B
Microsoft released an open-weight multimodal reasoning model. 15B parameters, sees images, thinks. Microsoft is opening up more models, building out the ecosystem.

Claude Code & Coding AI

Three Claude Code releases this week:

v2.1.69 (yesterday!) — big release, 103 changes. New /claude-api skill for building apps with the Claude API. Improved /remote-control. Ctrl+U on an empty bash prompt closes bash mode.

v2.1.68
Opus 4.6 default reasoning effort lowered to medium (the sweet spot between speed and accuracy). The keyword “ultrathink” is back for high effort on demand. Old Opus 4 and 4.1 models removed.

v2.1.63
New /simplify and /batch commands. Project configs and auto memory now work across git worktrees. New env var ENABLE_CLAUDEAI_MCP_SERVERS=false to disable MCP servers from claude.ai.

From the community:

– Best Practices repo — a repo with all the tips and workflows in one place. Already 5,000 stars. GitHub – Free Max x20 for open source — Anthropic is giving 6 months of Claude Max (20x) to open-source maintainers with 5K+ stars or 1M+ monthly NPM downloads.

Interlude

Knuth and Claude — 88 Years Old, 30 Attempts, 1 Proof

Donald Knuth — computer science legend, author of “The Art of Computer Programming” (the bible of algorithms) — used Claude to solve a math problem. He’s 88.

Claude generated 30 different attempts at a solution. Knuth reviewed EVERY one of them, picked the one that worked empirically, and wrote the formal mathematical proof himself.

The internet immediately called it “vibemathing.” But this is the EXACT OPPOSITE of vibemathing. Knuth didn’t blindly trust the AI — he used it as a brainstorming partner, then applied human verification at the highest possible level.

This might be the most beautiful example of how we should be using AI: the machine generates options, the human verifies and proves. Especially when that human is 88 years old and still doing it better than most of us.

Tools of the Week

NotebookLM Cinematic Video Overviews (released March 4!) — Google added a feature to NotebookLM that turns your documents into animated explainer films. Not slides with narration — full animated scenes with a storyline. Under the hood: Gemini 3 plans the narration, Veo 3 generates the animation, Nano Banana Pro creates the graphic assets. Drop in a PDF, meeting notes, or a product spec → get a mini-film. For now, Ultra subscribers only and English only. Google Blog

GitNexus
open-source tool that turns any GitHub repo into an interactive knowledge graph + AI agent you can talk to about your code. Runs entirely in the browser. Has an MCP server with 7 tools: search, symbol lookup, blast radius, git-diff impact mapping. 7.3K stars. Same trend as the LSP topic above — giving AI better tools to understand code. GitHub

AI at Tenvalleys

CV Builder

This is a new section — every week we’ll share how we use AI in our day-to-day work at Tenvalleys.

We built a Claude Code skill that automates preparing CVs for client proposals. You drop in an old CV, a LinkedIn profile, or even raw notes from a conversation — and out comes a professional CV in the Tenvalleys branded template. HTML rendered to PDF via headless Chrome.

What it does:

– Generates a CV in the Tenvalleys branded template (two-column layout, A4, 3 density presets) – Writes bullet points using the CAR method (Context-Action-Result) — not “worked on projects,” but concrete achievements – Updates the CV from Linear data — projects, technologies, roles – Job-match — compares the CV to client requirements and produces a fit report – Searches our CV database for the best people for a specific role – Built-in quality checklist — the AI checks itself for hallucinations

It saves a lot of time on a process that used to be manual and slow. The same approach scales to other “we have lots of structured text, we need branded output, the AI does the first 80%” workflows.

If you’d like to automate something similar inside your own organisation — reach out at contact@tenvalleys.com.

In the Background

Claude #1 in the App Store
Claude overtook ChatGPT as the most popular app in the App Store. A big chunk of that is fallout from the OpenAI/Pentagon controversy — Anthropic refused to remove safety guardrails, users voted with their wallets.

OpenAI VP moves to Anthropic
the VP responsible for Post-Training (RLHF, safety, instructions) left OpenAI for Anthropic. Not a random engineer — someone who had direct influence over how GPT “thinks.” OpenAI is losing not just users, but key people too.

OpenAI raises $110B
record private funding round. Meanwhile they’re losing people and users. Lots of money, lots of questions.

Hot Take

Vibe Coding in AR Glasses, While Doing the Dishes

Someone posted on Reddit: “Vibe coding while doing the dishes in augmented reality.” The guy is literally coding in AR glasses while washing dishes.

On one hand — Knuth, in the same week, shows that the best results come from AI + careful human verification. On the other — someone’s coding at the kitchen sink because “the AI’s going to write the code anyway, I just nudge it.” And in the background, research says AI scores 84% on coding benchmarks but 25% on real production code.

Three completely different approaches to AI in one week.

Reddit — Vibe Coding While Doing Dishes in AR

See you next week.

Prepared at Tenvalleys — a delivery-first AI engineering partner — by Nikola Powałka. Feedback? Email us at contact@tenvalleys.com or reach out on LinkedIn.