Weekly News Roundup: CW39 - Cheaper models, and the software around them

Anthropic’s Opus 5.5 arrived on Tuesday with a claim the company makes: it performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5. Grok 4.7 had landed the day before at Grok 4.6’s price and speed, and OpenAI halved the price of GPT-6 Sol and Luna the same day as Opus 5.5. The same week, Epoch AI published the measurement that frames all three: at a fixed level of performance, the cost of AI has fallen about 47% per quarter since 2023, roughly 13 times a year.

The rest of the week was about what sits around the model. The agent harness, the software that decides what a model sees, touches and remembers, got a definition worth arguing with and a fifteen-minute build recipe. Microsoft rebuilt Copilot around three tabs, one of them a persistent agent that keeps working while you are away. And Anthropic’s new biology group reported that Claude agents had picked a CRISPR-like repeat array out of a phage genome, a finding the company says it cannot yet explain.

tl;dr

• 🧾 Opus 5.5, and the price of a task: a 40% lower cost to run than Opus 5, list prices 20% below it, and a separate argument about what a finished task costs.
• 🪙 Cheaper intelligence, three ways: OpenAI halved GPT-6 Sol and Luna, xAI held Grok 4.7 at Grok 4.6’s price, and Epoch AI puts the fall in the cost of a fixed performance level at 47% a quarter.
• 🧰 The harness gets a name: a vendor explainer defines the part of the stack you own, a checklist post prescribes it, and a teaching harness turns a coding agent into a tutor.
• 🗂️ The new Copilot has three tabs: Home, Code and Autopilot, the last a persistent agent that lives in your tenant and works without a prompt.
• 🧬 Claude finds CRISPR-like repeats with no known function: Claude agents surfaced an array beside a phage reverse transcriptase, and neither the enzyme’s activity nor the system’s purpose has been shown.
• 🤖 Hermes, still growing up: a live screen for bots on remote hosts, a search integration advertised as free across tiers that the docs tie to a paid subscription, a community plugin bundle and an index.
• 🔐 A security model made by pruning another model: Aikido’s Altar-1 keeps 168 of GLM-5.3’s 256 experts, which is a compression result rather than a model trained for security.

Opus 5.5, and the price of a task

The launch page gives each of its numbers a scope. Opus 5.5 performs at the level of Fable 5.1 on most work, and the company’s own tests put it at 40% less than Opus 5 on typical workloads at default settings. List prices are $4 and $20 per million input and output tokens, 20% below Opus 5, with cache reads at $0.20, which the page puts 60% below Opus 5. A fourth figure sits lower on the page, under cost and speed: the page’s own sentence is that Opus 5.5 generates output more than 30% faster than Opus 5. Anthropic says external evaluators including Frontier Design and METR tested it before release, and that this is its first release since it called for pacing the frontier, which is the explicit line back to CW38’s proposal-versus-process section.

On Anthropic’s own charts, Opus 5.5 sits ahead of Fable 5.1 on every evaluation the page lists, and ahead of GPT-6 Astra on most but not all: Astra’s 41.4% on AutomationBench is above Opus 5.5’s 40.0%, and its 64.6% on Terminal-Bench-Science 0.1 is above Opus 5.5’s 58.7%. Two footnotes sit under that table. The GPT-6 and GPT-5.6 figures are, in Anthropic’s words, as reported by OpenAI. The AutomationBench runs were done by Zapier without fallback models, so a safeguard intervention counted as a failure, which Anthropic says understates the score Opus 5.5 reaches in practice.

The more useful number is what a task costs. Addy Osmani’s explainer (25 September) makes the case that you do not buy tokens, you buy a finished task, and that two models at the same token price can cost very different amounts on the same job, because every turn resends the conversation so far and the model needing more turns costs more. He walks through turns, cache reads and output at list prices, states the tradeoff up front (every way to spend fewer tokens can also cost you a finished task), and says which of his numbers are illustrative rather than measured.

A second explainer makes effort the same argument from the other side: how much compute you want spent, closer to how long you would give a person than to a difficulty setting, with a loop of interview on low effort, implement on low effort, review, verify on high effort. Both are published on Anthropic’s dev site, and the effort piece is an Anthropic engineer’s own tests.

The most detailed vendor-published engineering write-up of the week is Anthropic’s own. How we made claude.ai 3x faster in two weeks reports an August sprint that took the core claude.ai and Claude desktop experience to about three times faster, with time to a typeable page at the 75th percentile falling from 3.1 seconds to 0.55 seconds. The work was done by Claude Tag (beta), running an internal research model roughly comparable to Opus 5.5, from a single Slack channel, with humans setting goals and approving every change: more than 3,000 merged changes and no customer-facing incident. It is Anthropic measuring Anthropic, and the fullest public account this week of an agent running the engineering loop.

The takeaway: the 40% is what it costs to run the model, the 20% is the list price cut underneath it, and the 30% is output speed. None of the three is cost per finished task.

Cheaper intelligence, three ways

OpenAI’s GPT-6 Sol and Luna landed on Tuesday with a 50% cut against GPT-5.6’s promotional prices: Sol from $4 to $2 per million input tokens and $20 to $10 output, Luna from $0.20 to $0.10 and $1.20 to $0.50. OpenAI attributes the reduction to caching and inference efficiency and says the savings are passed on. Both are in ChatGPT Work and Codex on the paid tiers, Luna also in the desktop app for Free and Go, neither in Chat yet. The benchmark claims are the vendor’s. A footnote on the Fable 5.1 datapoint in that cost comparison says it understates Fable 5.1’s real cost, because it omits the Opus 5 fallbacks that ran on roughly 40% of tasks.

xAI’s Grok 4.7 came out on Monday at $2 and $6 per million tokens, with a fast variant at twice the output speed and twice the price, and the body of the page says it is served at the same price and speed as Grok 4.6. The headline above it says twice as fast, at half the price of comparable models. Both can hold, because the second compares xAI’s model with other labs’ rather than with Grok 4.6, but the page never states which comparison each claim rests on. It is branded SpaceXAI while GitHub’s changelog still calls the vendor xAI, and no rename could be verified either way.

Both cuts sit on a measurement from Epoch AI. The plunging price of thought, by Luke Emberson and David Roodman, puts the decline at about 47% per quarter at a fixed level of performance, across five primary benchmarks covering maths, science and games, with data from 2023. That is roughly 13 times a year, the same claim in a different unit rather than a second finding. Epoch’s comparisons are its own estimates: about 4x faster than DNA sequencing, 6x than compute and 18x than lithium batteries. It lists its limits in the same document, including benchmark overfitting and users who do not sit on the cost frontier. A parallel analysis, on a different dataset covering knowledge, reasoning and software engineering, reaches a smaller number, 5 to 10 times a year, and isolates algorithmic progress at about 3x. Epoch’s 13x is a defensible reading of its own method, not a consensus.

The harness gets a name

The software that decides what a model sees, touches and remembers got a plain definition in August, and it kept resurfacing this week. Earendil’s What is a Harness?, published on 20 August, is written for people who have been too embarrassed to ask: four jobs, a system prompt that governs how the model responds, the tools it is given, the loop that runs it, and a translation layer that lets one harness drive models from different providers. The slogan it quotes is agent = model + harness. Earendil builds Pi and Lefos, so its account of the field, including which harnesses count as open and neutral, is its own.

A long checklist post dated 29 August is the prescriptive version. Its seven jobs: turn the request into a contract; give the agent a map rather than a manual; expose the right tools in the right environment; externalise memory into durable state; add sensors before autonomy; enforce permissions outside the model; and record traces and recover locally. Two principles carry: important rules should be encoded twice, once as guidance and once as a mechanical check, and the brain, the hands and the history should be separable, so a failed sandbox does not take the history with it. The post quotes Anthropic’s work on long-running harnesses, which is where its more specific claims come from, and it exists to promote a newsletter. Its replies include one replier’s personal test in which a generated repository map lost to no map at all, the agent answering faster and citing nothing.

The in-week version was a fifteen-minute workshop posted on 25 September: start with the Claude Agent SDK, because the harness handles loops, context and sandboxing; separate the brain from the hands with a sandboxed tool surface; run server-side and log every step so a closed laptop does not stop the run; make failure cheap; then turn yesterday’s logs into memory and skills. The SDK documentation is the primary here, and draws one useful distinction: the Agent SDK runs the loop in your process, the Client SDK makes you implement the loop, and Managed Agents is hosted.

The most interesting item in the cluster is a teaching harness. Gnos, MIT-licensed and created on 12 September, turns a coding agent into a teacher: it asks how deep you want to go, plans a route from syllabi or textbooks, builds a lesson, and records where your reasoning broke so the next one adjusts. It ships as a Codex plugin and a Claude Code plugin, and it was shared to r/hermesagent, where the comments ask whether it works with Hermes. It is not a Hermes plugin.

The takeaway: definitions and checklists travelled further this week than product announcements, which is what a layer you can own, inspect and version looks like.

The new Copilot has three tabs

Microsoft announced three tabs for its Copilot app on 25 September, and the third carries the agent. Microsoft describes Home as the new starting point, where Chat and Cowork come together with Word, Excel and PowerPoint built into the experience, and Code as the place to build small purpose-built solutions, powered by the same underlying technology as GitHub Copilot and running in a sandbox that can be hosted inside the customer’s own tenant. Autopilot, previously Scout, is an agent you give a name, a role and a goal.

Microsoft describes Autopilot as cloud-hosted and living in the tenant with its own identity, memory, computer and workspace, mentionable like a colleague in Teams, Outlook and documents, watching channels, following up on threads and picking a project back up days later without a prompt. That is a description of intent. The rollout order is concrete: Home and Code begin in the Frontier programme in the coming weeks, Autopilot reaches private preview at the end of the month, and Code arrives in preview for Microsoft 365 Premium and Pro subscribers later this year. CNBC reports that Cowork, Code and Autopilot are billed by usage rather than per seat.

Claude finds CRISPR-like repeats with no known function

Anthropic’s life sciences group reported on 23 September that Claude agents searching a DNA database for new reverse transcriptases noticed a repeating pattern beside an unusual gene and filed it for human review. The post’s own figures: roughly 950 agents, 21 hours, 210 million tokens, more than 200,000 reverse transcriptases gathered, 3,500 candidate systems, and the 20 most compelling analysed. One agent’s transcript, excerpted, reads: “I can see by eye a tandem repeat array … that’s a CRISPR-like … repeat array?!”

The finding, described in a preprint that Anthropic says is not peer reviewed, is a family of jumbo-phage reverse transcriptases paired with an array of units the preprint measures at about 200 nucleotides each and a partner gene of unknown function, which the company calls array-associated reverse transcriptases. The arrays resemble CRISPR arrays. The lab’s first experiments show the array is expressed as a set of distinct short RNAs; separately, published data from a Staphylococcus phage SA1 infection show those RNAs are highly expressed. That is where it stops. Anthropic says it does not yet know the system’s function, and the preprint’s limits are its own: the enzyme’s activity has not been shown, whether the unit RNAs are its substrates is unknown, and what the system does for the phage is unknown. Anthropic says Feng Zhang of MIT and the Broad Institute, who reviewed the preprint, called the identification of RNA-repeat arrays “genuinely intriguing” and said it merits further investigation. It is a remark about the repeats, not a confirmation of what they do.

The post credits Claude agents with the find. The preprint attributes the anomaly-spotting to specific Mythos 5 internal signals that respond to repeated DNA, then reports its own controls on them: both signals examined respond to repeats that are not DNA, so neither is specific to DNA. The sentence that holds is narrow: Claude agents surfaced a repeat array beside a reverse transcriptase in a phage genome, the pairing is a family nobody had described, and nothing about what the system does has been shown.

The takeaway: the finding is the array and its pairing, not a function. A preprint whose central molecule has not been shown to work is reporting an anomaly, which is what its own authors say it is.

Hermes, still growing up

CW38’s edition covered a Hermes refactor run by 1,393 subagents. This week’s follow-ups are smaller and more practical. Bot Screen, announced on 23 September, gives each bot its own desktop on a headless Linux gateway host and streams it into Hermes Desktop, so you can watch a bot drive a browser, then take over the keyboard and mouse for a login, a 2FA prompt, a CAPTCHA or a payment and hand control back afterwards. The bot continues in the session you signed into, and the screen lives on the host, so closing the app does not stop it.

The documentation specifies how the fence is built. Xvnc serves the display and the RFB protocol from one process per profile, listening only on a 0600 Unix socket with no TCP port and no VNC password, and control is a lease file, so the gateway drops keyboard, pointer and clipboard messages from any viewer that does not hold it. The docs also state the limits: the fence is tool-level rather than OS-level, so any process running as the gateway’s OS user, including the bot’s own terminal tool, can reach the display socket and the lease file directly. Chromium’s DevTools port is reachable by any local user, and the 30-second ticket travels as a URL query parameter on purpose, so a reverse proxy’s log may record one already spent. Linux gateway hosts only, for now.

The other announcement was search. Nous says it has integrated Perplexity’s Fast Search as the default for Portal subscribers, at 160 ms per call at the 50th percentile, and that it is free for all Nous Portal tiers. The documentation describes the Portal route as available if you have a paid Nous Portal subscription, which is not obviously the same claim. Both are in the record and neither settles it, so the narrow sentence they support is that Nous says it will not charge a separate fee for search across Portal tiers.

Two community projects do different work. oh-my-hermes installs as a skills tap and describes itself as an operating layer above native skills: it frames a problem, picks a workflow and an evidence gate, and runs native skills as capabilities inside that path. The post promoting it claims 113+ skills, auto-routing and a memory system, while the repository describes routing, evidence boundaries and taps, so the counts are the poster’s. Hermes Agent Archive indexes what people build, and it states its method: numbers from public API snapshots with the last successful fetch shown, an editorial ranking for usefulness with popularity breaking ties, and entry counts that are the poster’s.

A security model made by pruning another model

Aikido’s model card for Altar-1 calls it the company’s first open-weight security model, and the card is more interesting than the phrase. It was not trained for security work. It is a prune of Z.AI’s GLM-5.3, a 753B mixture-of-experts model, using REAP, which scores each expert’s real contribution and deletes the least useful, keeping 168 of 256 experts with no retraining, after which the routed experts are quantised to INT4. Routing is untouched, so about 40B parameters are active per token, as before the cut. Stored size is 328 GB and serving needs four H200s. The card reports 0.506 nats of KL divergence against the full-precision model on a sealed 25-prompt panel, against 0.511 for a different quantisation of the same cut. Its title says 504B parameters while its metadata says 501B, so about 500B is the figure to quote. Aikido’s pitch is that the weights run on-premises for customers who cannot send source code to an outside provider.

The Metasploit walkthrough that circulated on 23 September is a reshare of an article from 6 June, updated in July. It documents an end-to-end agentic penetration test in the author’s own lab, with Claude Desktop driving the Metasploit Framework through a Model Context Protocol server: scanning, launching exploit modules, managing sessions, a pivot from a Linux host to a Windows Domain Controller, and a permission gate keeping a human in control of every offensive step.

Prompt Injection in the Wild is the opposite kind of artefact: a tracker of publicly reported prompt-injection techniques where each entry separates the delivery method, the encoding and the propagation behaviour, and records the models it was confirmed against with a link to the reporting. Every non-blank cell comes from an explicit statement in the cited source, a blank means not stated rather than none, and each entry is marked Confirmed or Not fully vetted, with the unvetted ones surfaced. The repository was created on 14 September and had three stars when read, and its dashboard is client-rendered, so what can be said here is how it handles evidence, not what any row contains.

Elsewhere this week

Leaving the main thread. Smaller discoveries worth telling that do not need six paragraphs of analysis.

Space arrived this week with a launch post titled “We just killed Dropbox”: by the site’s own description it is a cloud filesystem that appears in Finder as a regular drive, stores terabytes of files without using disk space and works across several Macs. The replies are the second half of the story. Its founder describes a streaming filesystem that translates system calls into byte range requests in the cloud, with end-to-end encryption still on the roadmap, and says regular files are fine above roughly 50 to 60 Mbps while 4K editing wants about 250 Mbps. One reply calls it a WAN-NAS of sorts, another says the price per unit of storage does not match what it claims to replace, and the founder answers a reviewer’s marketing advice with a question: did I ragebait too hard. Nothing here says Dropbox is dead. A founder announced his own product.

Block’s Buzz Desktop v0.5.24 and v0.5.25 shipped on 23 and 24 September. v0.5.24 adds separate conversations per agent or per thread, clearer setup, remembered inbox filtering and better startup recovery. v0.5.25 adds canvas version history with atomic restore, running a prepared ACP task from a file or standard input, and opt-in newest-first thread windows on the relay.

Google Research’s video co-director orchestrates Gemini and Veo into long-form video that holds together, aimed at the two failure modes of chaining clips: characters and scenery drifting between shots, and an early artefact corrupting everything after it, which the authors frame as credit assignment. The authors report a peak quality score of 81.4 on GenAD-Bench, and attribute a 21.6% background-continuity improvement to CANVAS, the storyboarding component. Research only: no product, no availability.

Mia’s AI Lab publishes free recipes for running frontier-class open models on one to four DGX Sparks or on RTX GPUs, with decode speeds the author measured on her own kit: 48.7 tokens per second for Qwen3.8 Flash Next on a single Spark, 51.0 for DeepSeek V4.1 Flash on three to four. The repositories carry the patches, launch scripts and dated result tables.

Pi 0.86.0 shipped on 19 September, before this window opened. Two entries: prompt cache warming, which keeps valuable prompt caches alive during long tool runs, and prompt and tool updates that survive a resume or a branch navigation while keeping cached prefixes intact.

Distill describes a private space for captured thoughts, with agents that sort what you save, scheduled routines such as a daily news digest, and end-to-end encrypted sync stored on your device. The post announcing it is dated 19 September, before this window opened, and says 2.0; the site’s download line says v1.5.1, and no release notes turned up for either, so what 2.0 contains is not stated.

Taste Skill is a set of portable SKILL.md files that steer a coding agent’s frontend output away from what the community calls AI slop: centred headers, three-column card grids, gradient buttons. It was published on 19 September, before this window opened, and the files load in any agent that reads SKILL.md.

The release post for draw.io for VS Code 1.10 says it adds inline editing of diagrams in Markdown, covering drawio code blocks, linked .drawio.svg files and Mermaid. The format underneath is plain XML, so the diagrams diff in git and an agent can read them.

Lauren Tan’s pstack is the public part of a talk whose headline claim, 2,500 pull requests shipped to production in a month, is her own and unaudited; the conference’s listing for the same talk says 2,000. The MIT-licensed plugin is playbook-driven, with /poteto-mode as the entry point, and it sends precisely-specified code to Sol, fast mechanical work to Grok, and prose and judgement to Fable.

One user iterated on redesigns of his personal site with Opus 5.5 under a Max subscription and cut a trailer from the iterations; another used it to plan, record and edit an animated marketing video, calling Codex when he needed an image. Neither setup claims anything about what reproduces.

The Spacecraft Hacker’s Handbook, by Andrzej Olchawa and Milenko Starcik, splits space systems into three attack surfaces and works through them: mission protocols, real vulnerabilities in mission-control software including Yamcs, OpenC3 COSMOS and F´ GDS, crashing flight software on NASA’s Core Flight System with crafted telecommands, and the techniques behind the 2022 Viasat KA-SAT attack. No Starch says September while its distributor lists 3 November, so the book is not out yet.

Cloudflare Quick Tunnels have been documented since 2021, so this is a discovery rather than news: one command starts an outbound-only connection and hands you a random trycloudflare.com subdomain that proxies to your local server, with no account and no DNS records. The documentation and the marketing page disagree on the limits, and the documentation is the one to trust: 200 concurrent requests, no Server-Sent Events, testing and development only.

Closing

If there was one theme this week, it was that a cheaper token is not the same thing as cheaper work. Opus 5.5’s percentages measure different things, OpenAI’s cut is a list price, and the only figure on offer that describes a fixed amount of capability belongs to Epoch AI. The engineering this edition kept returning to was in the harness, the sandbox and the lease file, the parts that decide what a model can reach. And the week’s most interesting discovery arrived with its own limits in the same document.

That’s the week. See you in CW40.