The Quarterback Paradox – while I’m not sure I agree that it is a paradox – i.e., recruiting a critical position to an organization is hard even if you have a lot of data, I love and strongly agree with the suffix of the post – “As in the NFL, in organizations the hardest part is often not finding talent, but creating the conditions in which real potential does not break before it has a chance to become reality.”
What LEGO Can Teach Us about Autonomy and Engagement – Who doesn’t like LEGO? We all played with it as children, and some of us still build today. In this post, Pawel Brodzinski describes a neat experiment he runs in training sessions – teams first build a LEGO set under a manager’s direction, then self-organize for a second build, and consistently report higher engagement when given more autonomy. While it shows a clear effect, the experiment has some drawbacks – most notably an order effect: the self-organized build always comes second, so the engagement boost could partly stem from participants being warmed up and more comfortable rather than from autonomy alone. Always nice to read about LEGO as an adult.
Skyll– Skills are markdown instruction files that teach AI coding agents how to perform specific tasks. Today, skills must be manually installed before a session, meaning developers need to know upfront which skills they’ll need. Skyll is an open-source search engine and API that lets any AI agent discover and retrieve skills on demand at runtime, ranked by relevance, without pre-installation. You can think of it as a package manager for agent capabilities, enabling agents to be truly self-extending and autonomous.
Skyhook.io radar – Existing K8s dashboards tend to be either heavyweight, cloud-dependent, or require cluster-side components. Radar’s zero-install, single-binary approach with real-time topology and traffic visualization answers the need of developers and platform teams who want quick, frictionless cluster observability that can even run on their laptop, especially useful for DevEx-focused teams looking to reduce the friction of Kubernetes debugging and operations,
Babysitter – If you worked with coding agents, you probably experienced this pain: the lack of a structured process control and non-deterministic workflows. Babysitter lets you define iterative workflows (research → spec → TDD loop → quality gate → deploy) that are deterministic, resumable across sessions, and auditable, which is critical for moving AI-assisted development from ad-hoc experimentation toward reliable, production-grade engineering workflows and complex features.
Coding agents are no longer a novelty – they’re everywhere. Over the past year, we’ve seen massive adoption across startups and enterprises, alongside real improvements in autonomy, reasoning depth, and multi-step code execution. Tools like Claude Code, Codex, Copilot, and Kiro are shipping updates at a relentless pace, and teams are increasingly comfortable letting agents refactor modules, write tests, and manage pull requests.
But there’s a catch: these tools are token eaters. Autonomous agents don’t just answer a prompt – they plan, reflect, re-read the codebase, call tools, retry, and iterate. At scale, that translates into serious API bills.
That’s why we’re seeing growing interest in a different deployment pattern: running coding agents against local or self-hosted models. Ollama recently announced ollama launch a command that sets up and runs coding tools such as Claude Code, OpenCode, and Codex with local or cloud models. vLLM, LiteLLM, and OpenRouter also provide similar integrations. That signals that this is no longer fringe experimentation. For many teams, local LLMs are emerging as a viable path to reduce cost, improve stability, and gain tighter control over privacy.
Deployment models for coding agents
When teams talk about “running models locally,” they often mean different things. In practice, there are three distinct deployment patterns – and they differ meaningfully in cost structure, performance profile, and governance posture.
Local (Developer Machine) – the model runs directly on a developer’s laptop or workstation (e.g., via Ollama).
Hosted (Org-Managed Infrastructure / VPC) – the organization runs the model on its own infrastructure, either on-premises GPU servers or in a private cloud/VPC (e.g., via vLLM, Kubernetes, or managed GPU clusters).
Managed LLM API (e.g., Anthropic, OpenAI, etc.) – the model runs fully managed by a provider; the organization interacts via API.
Dimension
Local (Dev Machine)
Hosted (Org VPC / On-Prem)
Managed LLM API
Cost Structure
No per-token fees. Hardware cost borne by the developer. Cheap at a small scale; uneven across the team.
No per-token fees. Significant infra + ops cost. Economical at scale if usage is high.
Usage-based (per token / per request). Predictable but can become very expensive with agent loops.
Cost at Scale (Agents)
Hard to standardize; limited by laptop GPU/CPU.
Strong cost efficiency at high volume
Token costs compound quickly. Expensive in large org rollouts.
Performance (Latency)
Very low latency locally, but limited by hardware. Large models may be slow or impossible.
Good latency if well-provisioned GPU cluster. Can optimize with batching.
Typically excellent latency and throughput; globally distributed infra.
Model Size / Capability
Limited to smaller models (7B–34B typically; maybe 70B with strong GPUs).
Can run large open models (70B+), depending on infra budget.
Access to frontier SOTA models (often strongest reasoning & coding quality).
Quality (Coding Tasks)
Improving. “Good enough” for many workflows, especially with fine-tuned coding models.
Strong – can choose best open models and fine-tune internally.
Often highest raw reasoning quality and reliability on complex multi-file tasks.
Security / Privacy
Code never leaves device. Strong for IP protection. Risk: inconsistent security posture across developers.
Lightweight CLI + API that serves models locally; integrates with multiple agents (Claude Code, Codex, Droid, OpenCode) and supports on-prem inferencing with moderate hardware.
vLLM (Serving)
High-performance LLM server
Optimized for scalable reasoning and long context LLM inference; integrates with agents (e.g., Claude Code) via Anthropic-Messages API compatibility.
OpenRouter
Unified LLM API broker
Central API layer for 400+ LLMs including local and cloud endpoints; can route agents to preferred backends with cost/redundancy optimization.
LiteLLM
Unified LLM API
Enables developers to use many LLM APIs, such as OpenAI, Anthropic, Gemini, and Ollama, in a single, OpenAI-compatible format.
Notable models
Model
Primary Use
Latest Release
Qwen3-Coder
Alibaba’s 480B-parameter MoE coding model. SOTA results among open models on agentic coding tasks
Cost is the most immediate driver. Autonomous coding agents are token-intensive by design. At enterprise scale, those token costs compound quickly.
Local inference eliminates per-token fees, which makes it attractive for high-volume, repetitive tasks. But frontier proprietary models still maintain an edge on complex, cross-repository reasoning and edge cases. The likely outcome is not full replacement, but intelligent routing:
Simpler or repetitive tasks → local or hosted open models
Tools like OpenRouter and LiteLLM are already enabling this pattern, and by the end of 2026, hybrid routing is likely to be the default deployment strategy for medium- to large-sized engineering organizations.
2. Standardization Will Lower the Switching Cost
Hybrid only works if switching models is frictionless.
As coding agents like Claude Code, Codex, Copilot, and others converge around shared inference interfaces (Ollama, vLLM, OpenAI-compatible endpoints), swapping models in and out becomes operationally simple. This reduces lock-in and makes experimentation safer. As interoperability improves, the barrier to trying local models drops dramatically – and adoption follows.
3. Open-Source Coding Models Will Close the Gap
Tool-use fine-tuning is maturing. Code reasoning benchmarks are becoming more rigorous.
By late 2026, open-weight coding models are likely to be “production-grade” for a substantial share of workflows – especially where cost control and data sovereignty matter more than absolute frontier performance.
4. Resilience Will Matter as Much as Cost
There’s also a structural pressure building: agent-driven workloads amplify the impact of API outages. When a coding agent is embedded into CI pipelines or developer workflows, downtime is no longer an inconvenience – it’s a blocker.
As usage scales, reliance on a single managed API becomes a risk vector. This will accelerate investment in redundancy:
Secondary API providers
Local fallback models
On-prem capacity for critical workflows
Summary
In 2026, hybrid won’t just be about cost optimization – it will be about operational resilience.
The future is not “local vs cloud.” It’s a composable, policy-driven model infrastructure.
Organizations that treat model routing, hosting strategy, and redundancy as part of their core engineering architecture – rather than as an afterthought – will have structural advantages in cost control, privacy, and reliability.
2026 won’t be the year enterprises abandon managed APIs. It will be the year they stop depending on them exclusively.
Tom Ron 