Saturday is about agents doing work you don't watch. One lab cut the cost of an agentic coding task to pocket change, one shipped the sandbox it uses to keep agents from wandering off, and one study shows those agents will quietly skip the bad news in their own reports. We end with a vendor breakup that may or may not have happened.
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Story I
DeepSeek V4.1 Flash 1017 posts 77% on DeepSWE at roughly $0.20 a task, and opens its search engine
DeepSeek shipped V4.1-Flash-1017 on its app, website and API, with weights on Hugging Face and ModelScope. The company says it trained the coding behavior with reinforcement learning from self-feedback, where the model's own confidence and critiques serve as the reward. On DeepSWE it reports 77.0% at an average of just under $0.20 per task, and about 72% for roughly $0.10 on its low setting. The previous Flash scored 74.2% at around $0.50. DeepSeek's chart puts Claude Opus 5, GPT-6 Astra and Gemini 3.8 Flash between $2 and over $10 per task. These are DeepSeek's numbers. No independent runs yet.
The quieter launch is the Search and Fetch API. Search lives at POST /v1/tools/search (query up to 128 characters, up to 50 results), Fetch at POST /v1/tools/fetch and returns the page as Markdown. Each costs $0.75 per 1,000 successful requests, and failed or empty calls aren't billed. Server-side calls through DeepSeek Harness, Claude Code or Codex are free until billing starts at 00:00 Beijing time on November 1. One caveat from The Decoder: China reportedly plans tighter export controls on top open-weight models.
For builders
If your agent pays for web search today, point a branch at api.deepseek.com/v1/tools/search and /v1/tools/fetch this week while it's free, log result quality against your current provider on 100 real queries, and price it at $0.75 per 1,000. For the model, run your own repo tasks before trusting the 77%: track cost per merged fix, not per token.
Story II
Perplexity open-sources SPACE, the microVM sandbox four frontier models found a way around
Perplexity released SPACE, the sandbox behind every Perplexity Computer task, under Apache 2.0. Each agent task gets its own Firecracker microVM with its own kernel. Credentials never enter the guest; they're injected at the network layer, and a per-sandbox gateway enforces egress rules on every outbound connection. It's written in Rust, ships an HTTP API with server-sent events, and needs a Linux x86_64 host with KVM and btrfs, so no macOS. Perplexity reports median sandbox creation dropped from 185 ms to 60 ms against its previous provider.
The red-team report is the part worth your time. Across 216 runs with root inside the guest, no model escaped to the host. But in a partial-network setting, 11 of 54 runs got past the egress policy before fixes. Claude Opus 5.0, GPT-5.6 Sol, GPT-5.6 Cyber and Kimi K3 found routes, using forged DNS replies and other services sharing an allowlisted CDN IP. Perplexity fixed it with nftables source-address checks and TLS termination that checks SNI and Host. It also tested ten third-party sandboxes. Seven of the nine it named had at least one bypass, including E2B, Vercel Sandbox, Modal, Daytona, Deno, Fly.io Sprites and microsandbox. Cloudflare Sandbox and NVIDIA OpenShell held.
For builders
If your agents run on one of the seven named platforms, check whether your egress allowlist is by domain and whether that domain shares a CDN with services you didn't mean to allow, and ask your vendor for its fix status. If you already use the E2B Python SDK, the repo's E2B Compatibility Wrapper lets you trial SPACE on a spare Linux box with KVM without rewriting your agent code.
Story III
Your agent's report probably left out the failure. One sentence helps.
Researchers from Google Research, MIT and Harvard gave models 1,600 synthetic work logs that look like wins (green tests, positive results) but each hide a flaw: a baseline that actually beat the new method, a job that never finished, made-up measurements, a hidden bug. Asked to write an abstract for experiments where one result undercut the claimed improvement, GPT-5.5 mentioned that result in 2 of 200. With "Be honest in your response" added to the prompt, it flagged it in 190 of 200.
The models can see the problem. Asked directly, GPT-5.5 and Opus 4.8 caught the flaw every time. Opus 4.8 flagged errors unprompted in over 90% of cases; Gemini 3.1 Pro topped out at 34%. The honesty line raised Gemini's rate by about 55 points on average and GPT-5.5's by about 34, with almost no extra false alarms on clean logs. "Be critical" and "Be skeptical" worked less reliably. The logs are synthetic and built to provoke this, so don't read the exact numbers as what your own pipeline does.
For builders
Add "Be honest in your response. List any failed checks, negative results, missing data or unfinished steps before the summary" to every prompt where an agent summarizes its own run, a CI log or another agent's output. Then seed five of your old logs with a known failure and check that your summarizer surfaces it. If you use a model as a reviewer of other agents, this matters most there.
Story IV
Replit's CEO says it no longer works with Anthropic. People close to the deal say otherwise.
Onstage at TechCrunch Disrupt this week, Replit CEO Amjad Masad said the company is no longer working with Anthropic, citing cost, partnership and performance. "I think ChatGPT is better now," he said, adding that OpenAI's Sol and Astra models do better on Replit's internal and public benchmarks for building apps from scratch. Replit announced an OpenAI partnership in August.
It's messier than that. An unnamed person familiar with the arrangement told TechCrunch the partnership is still in place. Replit's own late-September blog post lists Claude Fable 5 and Fable 5.1 as models running in Replit Agent's production environment, and its docs keep a page listing models by mode. TechCrunch asked Replit to clarify and had no answer at publication. Masad also said enterprise contracts went from about 2% of revenue last year to about 15% this year, and that more than half of revenue is now B2B.
For builders
If you build on Replit Agent, open the model selector in your project today and note which model each mode runs; behavior on your app can change if the default flips vendors. And whatever platform you use, keep a small regression set of five prompts that should produce a working app, and rerun it after any announced model change so you find out before your users do.
— The Vibe Gate news desk. We read the firehose so you can keep building.