AUTHOR
Simon Maple
Simon Maple is the Head of Developer Relations at Tessl, and AI Native Dev co-host. Previously, Simon was the Field CTO, and VP Developer Relations at Snyk, ZeroTurnaround, and IBM. He became a Java Champion in 2014, JavaOne Rockstar speaker in 2014 and 2017, Duke’s Choice award winner, Virtual JUG founder and organiser, and London Java Community co-leader.
Articles
Article
Add agentic code review to your repos in 5 minutes
Learn how to set up an automated code reviewer in minutes, using Tessl Code Review to evaluate pull requests based on your team's standards and improve code quality.
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Verifiers: enforce your repo's rules on every agent change
Tessl Verifiers are JSON-based rules in your repo that enforce coding standards on every code change, ensuring consistent rule checks beyond what linters can provide.
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Anatomy of an Agentic Code Review
Explore how coding agents transform code review processes by increasing PR volumes, highlighting the need for agentic workflows to handle rapid development cycles.
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A context driven code review that learns and improves on every run
Tessl's context-driven code review integrates with software development, improving code generation and maintenance by learning from past reviews and enhancing standards.
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Jev is 13.6x faster and 2.7x cheaper than GPT Luna 6 for Tessl verifiers. Try it yourself.
Jev, TypeSafe's new decision model, is 13.6x faster and 2.7x cheaper than GPT Luna 6 for Tessl verifiers, offering a cost-effective and efficient solution.
Article
Who Owns your/the Context?
Explore the complexities of context ownership in agentic transformations, focusing on workflows, processes, and the critical role of explicit ownership models.
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Feedback Loop Engineering: making your project agent-ready
Explore feedback loop engineering, focusing on designing loops around agents to improve project conditions, enhancing autonomy and effectiveness in agentic coding.
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Review Agent-Written Code Against Your Team's Standards
Tessl Code Review aligns with team standards, reviewing entire PRs and tracking changes, tailored for code written by AI agents rather than humans.
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The new Tessl review: now you decide what "good" looks like:
The new Tessl review lets users define their own criteria for skill quality, offers agent-based accuracy, and maintains a history of review runs.
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Same quality, a quarter of the cost: Should DeepSeek Flash be your model of choice?
DeepSeek Flash offers comparable quality to pricier models at a fraction of the cost, making it a cost-effective choice for running agentic tasks at scale.
Article
Opus 4.8 tops the LLM leaderboard with 95% on skill evals
Opus 4.8 leads the LLM leaderboard with a 95% skill evaluation score, surpassing Opus 4.7 and Composer 2.5 Fast, despite being the slowest model tested.
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We ran Composer 2.5 and 2.5 Fast across 11 skills. Surprisingly, Fast won.
Composer 2.5 Fast outperformed Composer 2.5 across 11 skills, scoring higher and running 32% quicker, while costing the same, challenging typical speed-quality trade-offs.