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
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
Your benchmarks are lying to you, and your judge is to blame!
Benchmarking AI models with single LLM judges can skew results due to judge bias. Multiple judges reveal score variations, suggesting a need for diverse evaluation methods.
Article
Stop trusting your agent skills with vibes. Eliminate the context security risk.
Learn how 'tessl-audit' helps secure AI agent plugins by scanning for vulnerabilities, assessing quality, and ensuring plugins enhance agent performance.