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All digests tagged Tessl
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Harness engineering is the infrastructure around LLMs, encompassing tools, context, guardrails, and telemetry. While LLMs introduce non-determinism, core software principles like CI/CD, security, and observability remain essential for building robust agents.
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Learn how to build a fully autonomous 'Software Factory' using agent loops and feedback loops. Discover how to achieve 75% PR merge rates without human intervention, while maintaining cost control and model agnosticism.
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AI attackers move at machine speed, finding 0-days by reversing open-source code. To defend, security must automate remediation, implement forensic-grade detection, and use deception (digital twins) to counter the threat.
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The panel discusses securing AI software factories, arguing that agents must be treated as unpredictable actors. Key controls include enforcing boundaries, never letting agents hold credentials, and securing the dynamic supply chain.
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Agentic coding is moving fast, creating massive efficiency but also unprecedented security risks. Experts discuss securing the 'software factory' and the need for new controls like identity and intent analysis.
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Learn how to build a 'software factory' using agent feedback loops. We cover inner, middle, and outer loops, and why clear intent is crucial for self-healing codebases.
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CTO Jacob Lauritzen details how Legora scales with AI agents, discussing custom orchestration tools, agentic bug-fix loops, and why measuring AI adoption by tokens is a flawed metric.
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The ROI in an AI software factory comes from the system surrounding the agent—context, verification, and feedback loops—not the agent itself. Learn how to measure the full cost of a PR.
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Scaling development with AI requires more than just generating code. Learn how automated code review, Jev classifiers, and robust CI/CD guardrails are essential to manage the flood of AI-generated PRs and maintain system reliability.
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Learn how to manage the exploding costs of AI agents. Experts discuss cache-aware routing, open-weight models, and why only $18 of every $100 spent on AI actually ships to users.
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Learn how to modernize legacy codebases by treating them like functioning cities, not 'balls of mud.' Discover how to decouple AI workflows from single providers using specialized servers and parallel agent processing.
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The future of software development is moving from writing code to defining specs. The focus must shift to 'context engineering' and building robust 'software factories' to manage AI agents.
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AI agents are democratizing software creation, but failure can occur when product context is missing. The shift requires Product Managers to become 'agent orchestrators' to manage the new wave of non-developer builders.
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Hugging Face's MCP server reveals that out of 10M protocol messages, only 62K are useful tool calls. Learn why stateful protocols are chatty and how Web MCP shifts agent focus from pixels to capabilities.
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Skills are transforming software development from revolving around code to intent. Experts argue that managing skill sprawl requires treating skills as code, focusing on context engineering over model size.
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Lada Kesseler details 'loop engineering' for AI agents, arguing that quality is achieved through iterative refinement (the centrifuge). She advises using deterministic verifiers and structured ground rules over expecting perfect first drafts.
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Scaling automation requires shifting focus from fixing agent-produced code to improving the underlying system. Success depends on establishing centralized, reusable platforms and mandates for context authoring.
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Don Syme discusses Continuous AI, extending CI/CD into subjective automation using GitHub Agentic Workflows. Key focus areas include bounding context and making the repo the unit of production.
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Agents introduce non-determinism, forcing a security shift from simple authentication to complex Mission Identity. Learn how new protocols like Agent Auth and Cross App Access are redefining trust boundaries.
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A live demonstration shows how running `tessl review run` on a Snyk skill improved its score from 87% to 90%. The session highlights progressive disclosure and using Agent Scan to catch security vulnerabilities.
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