a quiet day lets us report on a long trend of the major coding agents
It has been a tale of two cities in the past 3 weeks since the launch of GPT 5.5; while the finance folks fall in love with Anthropic’s growth and CFO ahead of its likely October IPO, there has been a notable rise in pro-Codex sentiment among AI Engineers, likely a combination of GPT 5.5 being a really good (in some scenarios Mythos-tier) model, launch of Codex for Everything Else, and, a third thing, which is the trigger for today’s op-ed: more generous limits.
The messaging for Claude’s pricing change was generally pretty well done, it is simply not what uses of alternative harnesses wanted to hear: every Claude subscription now gets a monthly credit of API tokens equal to the dollar amount of the Claude subscription plan. So you pay $200, you get BOTH a Claude subscription with its own limits for using Claude on Anthropic-owned harnesses like Claude.ai and Claude Code (“interactive usage”), AND $200 worth of API credits for using Claude everywhere else including claude-p
, OpenClaw and others (“programmatic usage”).
If things had worked this way from the start, it would have been viewed as a very good deal: However, because of the historical subsidy/pricing advantages (estimated between 70-90% discount from API pricing), people are viewing it as a “rug pull” of sorts — however it’s nice to have an official policy in place as opposed to the selective targeting of OpenClaw, OpenCode, and uncertain status of less popular harnesses.
That these headlines come on the same day as OpenAI launches their enterprise switch promo is an incredible coincidence:
At the end of the day, we would caution against reading too much into swings either way - both labs are doing very well, and these are in the grand scheme of things normal pricing shifts by people inventing the future of coding while figuring out optimal pricing as they shake up a decades-old industry. Anthropic was more liberal in the beginning, but now that Claude Code has a sustainable brand and clout as an agent harness, Anthropic is putting its most favorable pricing behind its own tools and metering everything else, whereas Codex as the challenger is being more liberal with everything.
Perhaps hardware is destiny, perhaps this is part of a longer 6 month alternating cycle of the “mandate equinox”:
AI News for 5/12/2026-5/13/2026. We checked 12 subreddits,
[544 Twitters]and no further Discords.[AINews’ website]lets you search all past issues. As a reminder,[AINews is now a section of Latent Space]. You can[opt in/out]of email frequencies!
AI Twitter Recap
Agent Infrastructure, Harnesses, and Developer Platforms
Cline, LangChain, Notion, and Cursor all pushed deeper into agent platform territory:Clineopen-sourced a rebuilt** Cline SDKand refreshed CLI with a TUI, agent teams, scheduled jobs, and connectors, positioning its harness as a reusable substrate for custom coding agents.LangChainshipped a large batch of agent lifecycle infrastructure at Interrupt:LangSmith Engine, SmithDB**,** Sandboxes**,** Managed Deep Agents**,** LLM Gateway**,** Context Hub**, and** Deep Agents 0.6**. The most technically notable piece isSmithDB, a purpose-built observability database for nested, long-running traces with large payloads, reportedly yielding12–15× faster access on key workloads; the team says it is built atopApache DataFusion and Vortex. In parallel,Notion’s External Agents APIlets third-party agents such as Claude, Codex, Cursor, Decagon, Warp, and Devin operate directly inside Notion as a shared, reviewable context layer rather than another silo.Cursorexpanded cloud agents with fully configureddevelopment environments including cloned repos, dependencies, version history, rollback, scoped egress, and isolated secrets.Agent UX is increasingly about long-running state, streaming, and orchestration rather than chat: Several launches converged on the same design direction.Duet Agentproposes a state-machine harness for jobs that lastweeks or months, with parent/sub-agent coordination and memory replacing compaction. LangChain’s OSS updates addedstreaming typed projections, checkpoint storage, code interpreter, harness profiles, and model-specific tuning, all aimed at richer agent event streams than plain tokens.Tabracadabramoved from autocomplete to a context-aware assistant in any textbox, whileVS Codeintroduced an Agents window and better multi-project task review. The architectural message across these releases is that production agents increasingly needdurable execution, inspectable intermediate state, and tool-native UI surfaces rather than stateless prompt/response loops.
Model Training, Architecture, and Data Efficiency
Pretraining efficiency and architectural experimentation were the strongest research throughline:Nous Research’s Token Superposition Trainingmodifies the early phase of pretraining so the model reads/predicts contiguous bags of tokens before reverting to standard next-token prediction; they report2–3× wall-clock speedup at matched FLOPs with no inference-time architecture change, validated from270M to 3B dense and10B-A1B MoE.Jonas Geiping et al.argued current message-based/chat training overly constrains agents to a single stream and released amulti-stream LLM paper claiming lower latency, cleaner separation of concerns, and more legible parallel reasoning/tool use; paper and code are linkedhere.δ-memproposed an external online associative memory attached to a frozen full-attention backbone, with an8×8 state reportedly improving average score by1.10× and beating non-δ-mem baselines by1.15×, with larger gains on memory-heavy benchmarks.** Post-training/compression and data curation also produced notable results**: NVIDIA’sStar Elasticclaims one post-training run can derive a family of reasoning model sizes, at360× lower cost than pretraining a family and7× better than SOTA compression. Datology’s VLM work, highlighted bySiddharth JoshiandPratyush Maini, arguesdata curation alone can produce major multimodal gains:+11.7 points across 20 public VLM benchmarks at 2B, beating InternVL3.5-2B by roughly** 10 pointsat about 17× less training compute**, and near-frontier 4B performance with** 3.3× lower response FLOPsthan Qwen3-VL-4B. On the open data side,Percy Liangsaid the next Marinrun already has 18T tokensin its mix and is still seeking more pretraining, mid-training, and SFT data, with a companion token viewershared here.Open evaluation and dataset work is maturing alongside model building:Kevin Li’s SWE-ZERO-12M-trajectoriesis positioned as the largest open agentic trace dataset:112B tokens, 12M trajectories, 122K PRs, 3K repos, 16 languages.Victor Mustarflagged llama-evalas a step toward more comparable llama.cpp community evals. Meanwhile,Steve RabinovichandSayash Kapoorargued credible agent evaluation requireslog analysis**, not outcome-only metrics, because stronger agents expose hidden benchmark bugs and reward-hacking paths.
Enterprise AI Pricing, Platform Competition, and Distribution
Anthropic vs OpenAI competition sharpened around enterprise distribution and developer lock-in:Ramp data cited by Andrew Curranshowed** Anthropic at 34.4%of businesses vs OpenAI at 32.3%in April, the first apparent lead change in business adoption;The Rundownamplified the same figures. At the same time, Anthropic changed plan economics:ClaudeDevs announcedthat paid Claude plans will get a dedicated monthly credit for programmatic usage across theAgent SDK**,claude -p
, GitHub Actions, and third-party SDK apps. This was immediately read by power users as a major restriction on subscription-subsidized harnesses, with criticism fromTheo,Jeremy Howard,Matt Pocock, andOmar Sanseviero. Anthropic partially offset that backlash with a separate50% increase in Claude Code weekly limitsthrough July 13, stacked on the previously announced 2× 5-hour limit increase.OpenAI responded aggressively with Codex enterprise incentives:OpenAI DevsandSam Altmanofferedtwo months of free Codex usage for enterprise customers switching in the next 30 days. OpenAI also published more technical platform detail, including aWindows sandbox design write-updescribing the combination of local users, firewall rules, ACLs, write-restricted tokens, DPAPI, and helper executables needed to safely run coding agents with local filesystem/tool access. The competitive dynamic now looks less like “best model wins” and more likesubsidy + workflow control + harness compatibility.** Enterprise adoption is increasingly tied to runtime/security assurances**:Perplexitydescribed a hardware-isolated sandbox architecture with VPC-level separation, short-lived proxy tokens, and scanning of external content before agent actions, withadditional detailson encryption and auto-deletion.Aravind Srinivasframed this as foundational to Perplexity becoming an enterprise knowledge/research platform. The broader pattern: agent vendors are no longer selling only intelligence; they’re sellingbounded execution environments.
Autonomous Science, Cyber Capability, and Robotics
Recursive self-improvement moved from idea to startup cluster: The largest single meta-theme was the launch ofRecursive, founded to build AI that automates science and safely improves itself. Launch posts fromRichard Socher,Josh Tobin,Dominik Schmidt,Jenny Zhang, andShengran Husuggest a team drawn from open-endedness, AI Scientist, and research automation work. In adjacent work,Adaption’s AutoScientistaims to automate the full training-research loop outside frontier labs, withSarah Hookerarguing that most model training failures are due to research-loop brittleness rather than mere compute scarcity.Cyber capability evaluations continue to steepen: The UKAI Security Institutesaid the length of cyber tasks frontier models can complete has been doubling every few months, and that recent models are beating prior trends. Anthropic/Glasswing’sLogan GrahamsaidClaude Mythos Preview is the first model to solve both AISI end-to-end cyber ranges, includingCooling Tower, and the only one to clear every task under the institute’s** 2.5M-tokencap. XBOW reportedly found “token-for-token, unprecedented precision,” and partner usage allegedly surfaced thousands of high/critical vulnerabilitiesin weeks. Independent commentary fromscaling01claimed a newer Mythos version completed a cyber range6/10 times vs 3/10** for the preview baseline.Robotics got a concrete long-horizon deployment demo:Figure’s Brett Adcockstreamed humanoid robots running a full** 8-hour autonomous shifton package sorting using Helix-02**, with follow-up details that the robots reason from camera pixels, operate around** human parity (~3s/package), perform on-device inference**, coordinate as a networked fleet, autonomously swap for low battery, and self-diagnose/fail over to maintenance when neededhere. This is one of the clearer public demonstrations ofmulti-robot, long-duration, no-human-in-the-loop orchestration rather than a short benchmark clip.
Top tweets (by engagement) Claude Code pricing and limits:@ClaudeDevs on 50% higher weekly limits,@ClaudeDevs on programmatic credits, and the ensuing developer backlash from@theomade pricing policy the day’s most consequential developer story.Codex enterprise push:@sama offering two free months of Codex usage for switchersand@OpenAIDevs’ enterprise call-to-actionsignaled an unusually direct go-to-market counterpunch.Figure’s 8-hour humanoid shift:@adcock_brett’s livestream postdrew enormous attention and is one of the few viral posts in the set with clear technical substance.Cline SDK launch:@cline’s SDK releasewas one of the highest-engagement genuinely technical launches, reflecting demand for open coding-agent harnesses.Token Superposition Training:@NousResearch’s TST poststood out as a rare pretraining-method tweet that broke through widely, likely because the claim—2–3× training speedup without changing inference-time architecture—is concrete and economically important.
AI Reddit Recap
/r/LocalLlama + /r/localLLM Recap
1. Efficient On-Device LLM Inference
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