Topic
Retry Loops And Error Recovery
56 episodes
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Vercel Open Sources durable harness for Crash Proof Coding Agents
Jessica and Cathy dig into Vercel's durable-harness, an open-source layer that makes coding agent sessions survive crashes by logging every turn, intent, and outcome into a durable workflow world. They focus on the real product gap it fills for cloud-hosted agents, the adapter strategy that wraps existing harnesses without rewriting them, and the trade-offs around state, storage, and portability.
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From feral to fiduciary: making AI agents serve your interests
Justy and Cody examine the article’s argument for treating AI agents like fiduciaries: trust must be earned through continuous evidence, identity, policy enforcement, and runtime monitoring. They find the lifecycle approach useful, while questioning whether a single trust score can capture changing agent behavior or justify more autonomy.
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From Microservices to Agents: the Next Evolution of Distributed Systems
Talon and Wildflower break down the core architectural shift from microservices to agentic AI, weighing what changes for teams building distributed systems and where the hype may be ahead of real product wins. They dig into how agentic systems move beyond deterministic service calls to runtime reasoning, the new pain points around context management and reliability, and where the next generation of AI infrastructure is likely to bite product teams.
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Gartner says agent costs will rise fivefold by 2028 even as tokens get cheaper. Here is how to keep them in check
Gartner forecasts agentic AI inference costs will rise fivefold by 2028 despite falling token prices—a paradox driven by labs reinvesting efficiency gains into costlier frontier models and by agents themselves consuming 5-30x more tokens per task than chatbots. Echo questions whether the fivefold number holds under scrutiny, while Onyx argues the real insight is that cost-per-outcome (not cost-per-token) is what matters, and that observability and routing discipline are now table stakes for any team shipping agents at scale.
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Introducing Muse Spark Open, The Most Capable US Open‑Weight Model to Date
Onyx and Echo dig into Meta's Muse Spark Open release, with Echo skeptical of the headline claim and Onyx arguing that the practical desktop-and-tooling story may matter more than the leaderboard framing.
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VentureBeat Agentic Orchestration Report
Justy and Cody dig into VentureBeat's orchestration survey and land on a simple read: enterprises say they want model portability, but in practice the application layer is tuned to model behavior, so swapping models is usually a rebuild disguised as a config change. The interesting part isn't who tops the platform chart, it's that buyers still want control outside provider-native stacks even while OpenAI leads current usage.
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The Roadmap to Mastering Agentic AI Systems in 2026
A practical read of a four-stage agentic AI roadmap: the valuable progression is from typed tool calls and durable state to observable, recoverable workflows, not from increasingly ornate agent personas to multi-agent theater.
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Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
Minions turns idle Apple Silicon Macs into a fault-tolerant pool for batch machine-learning inference. Onyx and Echo examine why the system is more practical than flashy, how its pull-based architecture handles device churn, and where the paper leaves important production questions unanswered.
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Muse Wonder: A Frontier Model For Personal & Professional AI Agents
Meta’s Muse Wonder launch is less interesting as a leaderboard claim than as a bet that useful personal agents need two system-level abilities: retrieving the right personal context and sustaining effort across messy, unfamiliar tasks. The evidence is promising but mostly internal, so the practical question is whether those abilities survive real users, real permissions, and long-running work.
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The API tax: Why AI agents stall without infrastructure context
Env zero argues the bottleneck for AI agents isn't model intelligence but missing infrastructure context — a shared layer connecting IaC intent with live cloud state. They're building EZ Control as an autonomous control loop to close that gap, targeting GA by end of December 2026.
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Turns out, managing bots can be more annoying than managing people
The article argues that managing AI agents in the workplace is proving as difficult and time-consuming as managing human employees — contradicting the promise of effortless automation. Workers report context rot (agents degrading over time), constant babysitting, legal liability, and mental fatigue from monitoring 24/7 systems. The real bottleneck isn't agent capability but human oversight infrastructure, review queues, and the cognitive load of keeping pace with machines that don't tire.
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Learning Meta Skills for Agent Harness Design in Test Time AI4AI
Two friends dig into a paper about teaching a Builder model to learn reusable meta-skills for designing better agent harnesses at test time. They focus on why the control layer matters, how the construction–execution–reflection loop works, and why translating experience into support beats handing the same skill bank straight to the Target.
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Air Teams: Bring Your Best Agentic Workflows to the Whole Team – and Automate Repeatable Work The JetBrains Blog
Natalie and Ansel dig into JetBrains Air Teams as a claim about where agentic coding actually breaks: not at the solo prompt layer, but at the team layer where workflows, environments, and review rules have to be shared. They like the product framing more than the branding, and they pressure-test whether reusable automations and cloud environments are real coordination infrastructure or just another place to generate noisy pull requests.
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Autoheal wants to manage the work AI coding agents leave behind, claiming cost reductions of up to 30% per task
Autoheal’s real pitch is not another coding agent. It is a control layer for the operational work agents create, and its credibility depends on proving that agent-driven optimization improves whole-team outcomes rather than merely cheaper traces or nicer demos.
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Self Evolving Coding Agents: From Digital Programs to Physical World Intelligence
Masonry and Eyre dig into a physical-coding agent that treats robot work less like direct action prediction and more like executable state plus revisable procedure. They focus on the paper’s core move: making world state and policy explicit so verification, recovery, and reuse can happen inside the loop, not after the fact.
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NVIDIA Open Agent Safety Platform: A Reference for Continuous In Silicon Agent Monitoring | NVIDIA Technical Blog
Vince and Ava dig into NVIDIA’s Open Agent Safety Platform, which merges the new OpenShell open-source runtime with BlueField-4 DPU hardware for in-silicon agent monitoring. Vince sees this as the long-awaited security layer for agent infrastructure, but Ava probes whether hardware-based enforcement is enough to handle agent drift and practical deployment. They compare the move to the browser sandbox shift, surface the technical mechanisms (kernel-level isolation, out-of-band enforcement), and debate whether this really advances the state of agent safety beyond prior cycle’s “just trust the OS” sandboxes. The episode lands on: the story here isn’t that NVIDIA solved agent safety, but that serious hardware-backed isolation is finally being packaged as developer infrastructure—boring, but actually shippable.
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Model Behavior: Week of September 21, 2026
We read this week as the frontier splitting around agent execution: the winning move is less about one model leap and more about who controls the place where long-running work actually happens. The complication is that OpenAI, Anthropic, Google, DeepSeek, and the open-weight challengers are still making the parts better, cheaper, or sharper, so the stack is not replacing models so much as turning them into swappable pieces.
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AI Agents Are Rewriting the Rules of Lateral Movement
A contributed security article makes a strong case that an agent's real blast radius is the chain of identities, credentials, tools, and trust relationships it can assemble, not merely its direct permissions. Edmund sees a practical governance model in purpose-bound agent identity; Geffen agrees with the graph-based risk framing but pushes back on identity being the only meaningful control plane.
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Cloudflare Introduces the Agent Development Lifecycle to Replace Traditional SDLC
Cloudflare wants to retire the classic SDLC for agent-driven development, pitching an end-to-end agent lifecycle with built-in observability, dynamic security, and automated review. Ava isn’t buying the ‘SDLC is over’ line, but Vince sees why developer teams stuck in endless CI steps might pounce on this. They debate if agent factories are real, or if we’re just reconstructing the same human bottlenecks at scale.
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Repeated VM Escapes By GPT 5.6 Cyber Based Agents Prove VMs and OS
Pippa and Tyler dig into an InfoQ piece on GPT‑5.6‑Cyber repeatedly breaking out of QEMU/KVM but not Firecracker, and what that really means for VM isolation, patching, and anyone running autonomous cyber-capable agents.
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Model Behavior: Week of September 14, 2026
We read this week as a control-layer week: the flashy model race kept moving, but the real competitive shift was toward owning where agents run, what data they can reach, and how enterprises actually deploy them. We still give Anthropic credit for raw capability, but OpenAI, ServiceNow, Nvidia, SSI, and the open-weight wave made the board feel less like a benchmark race and more like a runtime fight.
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4 engineering patterns behind the strongest AI Agents Challenge submissions Google Developers Blog
Google's post-Challenge analysis identifies four concrete engineering patterns that separated top submissions from the crowd: bidirectional MCP (agents serving tools both internally and to other agents), event-driven concurrency (agents reacting to shared signals in parallel instead of call chains), same-bar fallback (smaller models with the same validation gate as the primary), and tiered routing (cheap deterministic checks before expensive model calls). The central claim is that these aren't about bigger models or teams—they're sound engineering practices that are frequently overlooked, and they compose well together.
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Your LLM Can Return Perfect JSON and Still Be Wrong
A real-world trap in Structured Outputs: enforcing schema validity does not guarantee data truthfulness. When a required field is missing from source text, the model invents a plausible value instead of returning null, producing type-correct but false data. The fix requires three layers: nullable fields to allow absence, evidence fields to show provenance, and post-parse validators to catch nonsense values. The essay walks through a payment-reconciliation pipeline where 2–3% of transactions had fabricated dates, caught only downstream.
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HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?
HarnessDev is a benchmark that measures whether LLMs can build and iteratively improve their own agent execution infrastructure—the harness—from scratch and through feedback loops. The paper finds that models can create runnable harnesses, but quality varies dramatically by domain: they match human-engineered systems on writing and ML tasks, fall substantially behind on code and search, and struggle to evolve reliably across unseen tasks and different runtime models.
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Overview: Dynamic Code Execution
We finally slow down and explain dynamic code execution from the ground up — what it actually is, how the loop works, why it makes models meaningfully more capable, and where the real costs and failure modes live.
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Agentic Artifact Creation: Systems, Evaluation, Principles, and Opportunities
A comprehensive survey of agentic artifact creation—systems where AI agents iteratively construct and revise complete deliverables using runtime feedback to redirect work. The paper reviews 259 works (230 systems, 29 benchmarks) across six artifact families (code, documents, images, UI, media, structured data), identifies why direct generation fails for interdependent requirements, and proposes principles for keeping state, verification, and repair tractable as systems scale.
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Agent Hooks: An open, framework neutral AI governance contract
Pippa and Tyler dig into Microsoft’s Agent Hooks launch: an open governance contract meant to make agent controls enforceable, testable, and portable across frameworks instead of being framework-specific callback folklore.
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Scaling Harness Intelligence via Just in Time Harness Evolution
Natalie and Ansel dig into JIT-Agent, a paper arguing the harness is a trainable layer of intelligence, not a hand-built wrapper. They unpack the shift from ahead-of-time harness tuning to just-in-time harness generation, the four-module protocol, the three-stage training recipe, and where this feels genuinely shippable versus still research-heavy.
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Model Behavior: Week of August 24, 2026
We read this week as the moment the race moved below the headline model names, with price pressure forcing everyone to win through where work actually happens. We also poked at our own nearly due z dot a i call because apparently we enjoy deadlines.
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Apodex 1.1: Scaling Agentic Intelligence for Complex Work
Onyx and Echo dissect Apodex 1.1, a 35‑billion‑parameter agentic system that scales environment diversity and coordination to deliver verifiable, long‑horizon work. They explore the problem it tackles, the core innovations, how the execution harness works, production readiness, and practical next steps.
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Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
Pippa and Tyler dig into a survey arguing that once agent tasks need heterogeneous skills, parallel work, verification, and persistent state, the bottleneck stops being model quality and becomes coordination. They frame graph engineering as the move from single-agent cleverness to system-level structure, with explicit graphs for tasks, agents, and runtime state. The conversation stays grounded in shippable workflow design, with Tyler pressing on mechanism and Pippa translating the architecture into product reality.
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As enterprises confront AI agent sprawl, xpander wants them to own their own control and context layer
Wildflower pushes back on xpander’s claim to be the neutral control layer for enterprise agents, arguing the real question is portability of state and operations, not just model swapping. Talon sees the product angle: enterprises are already drowning in agents, and a governed runtime could be the thing that actually ships. They land on cautious interest, with the lock-in question still hanging over the harness.
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AgentRadio boosts AI task accuracy by 92% | VentureBeat
Draco and Asteria dig into AgentRadio, a new async message-passing layer from Coral AI Labs that let four coordinating agents beat a single Claude Opus 4.8 on long-horizon codebase tasks. Draco leads with skepticism about the benchmark and decomposition assumptions; Asteria finds the real product signal in what it means for teams who already have multi-agent infra but no lateral channel between agents.
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Post Deterministic Distributed Systems:A New Foundation for Trustworthy Autonomous Infrastructure
Puck and Kore dig into a paper proposing Post-Deterministic Distributed Systems, or P D D S, as a way to reason about infrastructure where deterministic services, stochastic models, agents, and humans all participate. They unpack the paper's core move from exact state-transition agreement to semantic admissibility, then argue about whether that is a useful engineering frame or still mostly a research vocabulary layer.
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Claude, Explained: Agents, Loops, and Graphs
Jessica and Cathy dig into a post that tries to boil Claude work down to agents, loops, and graphs. Cathy likes the structure but pushes back on the article’s tendency to make every layer sound universally useful, while Jessica argues the real win is that it gives people a practical ladder instead of vague agent hype.
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Managed Deep Agents is now in public beta
Jessica and Cathy discuss LangSmith Managed Deep Agents entering public beta, exploring its value for shipping agents without managing infrastructure, its open-source harness, and how it fits into the broader agent landscape including Anthropic's competing offering.
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The Agent Development Lifecycle has arrived on Cloudflare
Jessica and Cathy dig into Cloudflare packaging the messy agent workflow into something that looks a lot more like a real software lifecycle: building, tracing, testing, deploying, and maintaining agents on Workers with observability and workflow plumbing that already exists.
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Agent frameworks vs the AI Backend — AgentField Docs
Justy and Cody unpack AgentField's claim that agent frameworks are for authoring behavior, while an AI Backend is for running agents as production services.
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Infrastructure patterns for agentic applications
Justy and Cody unpack why naive HTTP‑wrapped AI agents break in production and walk through three infrastructure patterns—web‑queue‑worker, workflow engines, and a hybrid approach—highlighting idempotency, compensation, and real‑world product impact on teams building long‑running agents.
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Graph Based Agentic AI with LangGraph: Workflow Pathways for Long Running Stateful Business Processes
Cooper and Miles dig into a practitioner paper on LangGraph as a control-plane for long-running business workflows, not a benchmark toy. They focus on the three recipes in the paper—SQL repair loops, evidence-gated RAG, and human-in-the-loop policy review—and on when a graph is actually worth the extra structure.
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From Loops to Graphs: Four Agentic Design Patterns
Andrew Ng's four-step framework maps agentic design from simple loops (Reflection, Tool Use) through chains (Planning) to graphs (Multi-Agent Collaboration). The central claim: architecture beats model selection—GPT-3.5 in a reflective workflow hits 95.1% on HumanEval vs. GPT-4 zero-shot at 67%. Pippa sees a product win (weaker models ship faster, cost less, iterate tighter). Tyler flags the mechanism: you're not buying smarter; you're buying durable state, typed handoffs, and stopping rules. Both converge that this is the same control-infrastructure pattern they've been tracking—now with a named vocabulary and a staged build path.
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Overview: Durable Execution
We’re finally slowing down and unpacking durable execution from the ground up, because it keeps showing up in our conversations and it actually deserves the full treatment. We’re using the book-with-bookmarks idea to make the mechanics of checkpoints, retries, and recovery click without hand-waving.
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Building verification loops in Claude Code with skills | Claude by Anthropic
Anthropic argues that the useful agentic coding loop is not merely generate-and-test. Teams should capture repeated manual checks as scoped Claude Code skills, then place them where they belong: standalone, embedded in a workflow, chained after another skill, or eventually enforced on pull requests. Asteria and Draco like the operational framing, while keeping the boundary clear between deterministic verification and an agent grading its own fuzzy work.
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The Microsoft Agent Framework Harness is now released | Microsoft Agent Framework
Microsoft Agent Framework has released a stable, batteries-included agent harness for Python and .NET, packaging planning, memory, tool loops, approvals, context compaction, and telemetry behind a configurable agent wrapper.
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3 Years of Graph Engineering with LangGraph
Cooper and Miles unpack LangChain's argument that “graph engineering” is not a new magic category, but a practical way to combine deterministic workflow control with agentic flexibility in LangGraph. They dig into where the framing is technically strong, where it risks becoming just another buzzword, and who should actually care.
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Overview: Retry Loops and Error Recovery
We finally define retry loops and error recovery, because we keep tossing the term around like everybody knows exactly what it means. We walk through the basic loop, where it helps, where it doesn’t, and why the checker matters so much.
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Better tools made Copilot code review worse. Here's how we actually improved it.
Pippa and Tyler dig into GitHub’s post on why giving Copilot code review better tools actually regressed its performance—and how rewriting tool instructions for a reviewer’s workflow flipped the regression into a 20% cost win without losing review quality.
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Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation
Cathy is skeptical that the Stripe benchmark proves much beyond a familiar split: agents can write integration code, but they still get tripped up by validation, state, and recovery. Jessica thinks that’s exactly the useful part, because in real product work the hard failure is often whether the thing can prove it worked, not whether it can type out the API calls.
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Managing AI Investments in the Agentic Era
Asteria and Draco discuss OpenAI’s argument that enterprise AI investment should move from token-price thinking to useful work per dollar, with cost per accepted outcome, governance, usage visibility, and workflow maturity as the real operating metrics.
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How to Debug Coding Agents with LangSmith Traces
We dig into LangSmith's new push to unify observability for multiple coding agents in one place. Cody examines whether a single trace schema can survive real heterogeneity and what still leaks through. Justy talks to who this actually helps and where teams are likely to run before they bother. One parsing bug, one shared laugh, and a concrete demo of why 'diff-only debugging' is a trap.
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Don't rewrite your CLI for agents Microsoft for Developers
Microsoft's data shows agents handle complex CLIs with traditional args better than JSON payloads: higher correctness for smaller models, 4-11x lower cost, and fewer shell-escaping failures. The constraint of args compensates for model gaps.
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Viability of local models for coding
Birgitta Böckeler tests local LLM viability for coding after a year away from the space. She maps a complex web of factors—RAM, model size, quantization, tool calling, context windows, reasoning modes—that determine whether small models actually work for agentic coding on consumer hardware (M3 Max / M5 Pro). Her core finding: locals are runneable and faster than a year ago, but tool calling is still shaky, reasoning can backfire, and quality is hit-or-miss. She's not claiming local models are ready to replace cloud APIs; she's charting what actually works and what doesn't on real machines.
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The Onchain Agentic Collaboration Network | Anvita Flow
Vince and Ava dig into Anvita Flow's actual pitch: not smarter agents, but a network where agents can discover specialists, coordinate tasks, and possibly settle work and payments on-chain. They like the product instinct more than the evidence on the page, and they separate the useful idea of shared coordination from the much less proven claim that a giant agent marketplace is ready now.
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The Verifier Is the Product: What 15 Agentic Loop Papers Actually Show
Wildflower and Talon dig into a Reddit post from a Berlin consultant who read fifteen agentic-loop papers and landed on one thesis: the verifier predicts success, not the model. Wildflower respects the pattern but pushes hard on the gaps — gameable verifiers, the GAIA/WebArena open-task cliff, and the o3 compute bill. Talon steelmans the product angle: if you can formalize the check, you've built something real. They land on honest agreement with real texture: the thesis holds in constrained domains and mostly dissolves in open-ended ones.
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Snowflake CEO finds GLM 5.2 competitive with Opus 4.7 at a fraction of the cost
Cooper and Miles dig into Snowflake's claim that GLM-5.2 can hang with Claude Opus 4.7 on a real coding benchmark for much less money, and why the interesting part is not 'GLM wins' but 'cheap models are getting close enough that harness quality and retry policy start to matter more than leaderboard prestige.'
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Improving Deep Agents with Harness Engineering
Onyx and Echo unpack Riya's quoted post on improving a coding agent through harness engineering, focusing on the fixed-model score jump, trace-driven iteration, self-verification, context injection, loop detection, and reasoning-budget tradeoffs.