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gs like rescuing or reconciling divergence among agents through things like a semantic rebase will be one of the core skills that separates effective agent orchestrators from everyone else it s not about being better at resolving git conflicts it s about recognising when mechanical rebase is futile and switching to intent preservation mode something i ve found helpful for codifying the semantic intention behind the code is domain tests and putting our documentation inside the repository so there s a shared understanding between users developers and agents extracting intent from code reading comprehension at scale exactly the skill yegge says is draining and beyond the capabilities of most devs reimplementing efficiently on the new baseline leveraging the new patterns rather than fighting them then making judgment calls about what to rescue and what to abandon this is ironically a task that the latest generation of ai agents are well suited for understanding intent and reimplementing which suggests the solution to the merge queue problem might be more agents but specialised prompts skills or sub agents with reconciliation focus that understand both branches and can perform a semantic rebase themselves tooling gaps current git tooling is entirely built for simple code reviews and mechanical merging git rebase git merge even fancy visual merge tools they re all operating at the surface textual level what we need for semantic rebasing to work 1 branch intent extraction what was this branch trying to do commit messages help but agents don t always write great ones 2 architectural diff visualization not just these files changed but the module structure shifted from x to y 3 conflict severity assessment is this a level 1 conflict or a level 3 conflict should i even try mechanical rebase 4 intent preservation tracking when i reimplement a feature how do i verify i preserved the original behavior i ve been experimenting with some of this in an open source experiment in visual source control at minimum being able to see which agent worktrees have diverged and by how much helps you prioritise what to merge first but the deeper tooling for semantic rebase or merging meaning is still nascent across the industry borrow this prompt until better tooling exists here s a prompt i ve been using when i need to perform a semantic rebase it works well with claude code openai codex or similar capable agents that have access to your codebase reconcile this feature branch with master so it can be merged cleanly do not change master only this feature branch warning master has undergone significant structural changes so this isn t a simple merge from master to reduce the diff suggested approach 1 first assess the scope compare this branch against master to understand what s diverged in both 2 merge master into this branch 3 resolve conflicts prioritizing master s architecture where there are structural disagreements 4 rebuild rescue feature functionality to work within master s current patterns 5 verify the feature still works as intended the goal is a pr that has no merge conflicts with master follows master s current architectural patterns preserves the feature s intended functionality note if the divergence is too severe it may be faster to cherry pick the feature s intent onto a fresh branch from master rather than wrestling with conflicts the key insight in this prompt is step 4 rebuild rescue you re explicitly giving the agent permission to abandon the original code in favor of reimplementing the intent and the escape hatch at the end acknowledges that sometimes even level 3 rebase isn t worth it sometimes you need to start fresh with just the intent the uncomfortable truth steve is skeptical that we ll get guardrails that make this accessible to average developers i share that skepticism semantic rebase requires the exact skill that s hardest and most draining reading and understanding large amounts of code in multiple versions and making judgment calls about what matters the developers who get good at this who can look at a hopelessly diverged branch and quickly extract its intent assess the new baseline and reimplement efficiently are going to have an enormous advantage it s not just about prompting it s about understanding codebases at a level where you can direct agents effectively the swarm is coming the merge queue is real and rebase is about to mean something much more interesting than git rebase i m making a desktop app for managing git repositories with ai agent worktrees open source and available for free on mac windows and linux if you re running multiple coding agents and want visibility into what they re all doing check it out posted on january 8 2026 january 9 2026 categories technology tags code review domain testing new software is often tested through a variety of methods such as unit testing feature testing and integration testing recently we ve been making some major upgrades to the icehouse ventures investor portal and we ve wanted to be sure that the changes to the software don t change the business logic i think the style of testing we re doing might be useful for other teams to explore we re calling this new style of testing domain testing domain testing focuses on validating the correctness of our application s business rules and domain specific logic rather than testing technical implementation details domain tests verify that the system produces the correct outcomes in realistic business scenarios in our venture capital context this means ensuring that complex financial workflows such as fund management rules or carry waterfall calculations behave as intended for example one of our domain tests verifies that when a startup company generates a return the correct amounts are shared across investors all software teams have been doing some kind of scenario testing forever in various forms but we haven t always had a consistent name for it domain testing focuses in on testing a specific business function the goal is to prove that real world business rules hold true using concrete examples that reflect how the software is actually used domain testing vs existing types of tests unit tests validate individual methods or classes in isolation checking that a specific function returns the expected number given certain inputs feature tests cover entire user stories or features potentially including the user interface controllers and the database often crossing multiple domains or services integration tests verify that different parts of the system interact correctly in laravel this might mean simulating an http request and asserting that the controller orm and database all work together domain tests validate specific business scenarios they don t necessarily go through the ui or http layer instead they check domain logic directly in the code the key difference is that domain tests explicitly encode business intent for example consider this finance module scenario given a fund of 10 million with an 8 preferred return hurdle when the fund earns 2 million of returns in its first year then the investors lps should receive their preferred return before any carry is distributed to the fund manager this reads like a real scenario it verifies that the carry distribution follows the defined waterfall a critical business rule without getting off track into implementation details test driven development for ai the long established practice of test driven development could take on new importance in the era of ai assisted development modern llms like claude and chatgpt are quickly evolving from simple code auto completion tools into reasoning agents that can create or update whole new features but as the scope of ai development widens the consequences of errors is becoming more serious a solid test suite serves as both a safety net and interestingly as a guide when an ai helps generate code llms are powerful but prone to hallucinations or subtle logic errors by writing tests upfront especially domain tests that define expected outcomes we create guardrails that keep the ai on track the developer specifies the correct business behaviour in the tests and the ai must work within those constraints this turns tdd into a way of teaching the ai what we actually want domain tests as executable documentation domain tests create a shared language of expectations that both humans and machines can understand written in terms of business scenarios using clear language and examples they serve as executable documentation of what the software should do developers product managers and even non technical stakeholders can read these scenario based tests and understand the system s intended behaviour the test suite becomes a living specification of the domain this clarity extends to ai systems too an llm assisting you can read your domain tests and see the business rules and constraints of your application if it proposes a refactor or new feature you can re run the domain tests to verify the ai didn t break any fundamental business rules our domain tests encode the business rules in a form that a machine can work with domain driven design ddd teaches us to model software around the core business domain in domain testing we leverage these same concepts one of the concepts we borrowed from ddd for our domain tests was inspectability meaning that you should be able to put the system in the test state and let a human look at it normal software tests are usually randomised and empheral not designed for human inspection using a specific repeatable scenarios is like the kobayashi maru simulator test in star trek the setup is consistent so you can explore different solutions that achieve the end goal practical tip we use laravel s factories and seeders to set up our domain scenarios this way we can quickly spin up a fake fund with ten fake investors each with certain ownership percentage then create transactions to simulate cash flows when your test reads like a story you know you re staying true to the domain driven spirit behaviour driven development bdd s given when then syntax models test scenarios in a way both developers and business stakeholders understand given a scenario setup when something happens in our system then the result is a predictable output this structured narrative helps communicate intent a finance expert could glance at this test and confirm yes that s exactly how the waterfall works you can implement this style in laravel with phpunit or pest the key is writing tests that tell a story set up context given perform an action when and describe the expected outcome then for normal tests we use arrange act assert which is also fine but slightly less suited to domain tests a practical way to calibrate ai and humans looking forward i think domain tests may become a crucial calibration tool between business intent and ai execution think of them as the training examples you provide to a junior developer except the dev will be an ai if an ai proposes a database schema change or generates a new laravel controller how do we ensure it didn t break fundamental business assumptions run the domain tests a comprehensive suite will immediately flag any business rule violations giving both human and ai rapid feedback domain tests also become a sandbox for business validation write a test that sets up a complex fund with edge cases different fee structures multiple closing dates run the domain logic and use laravel s debugging tools to inspect the output it s like a flight simulator for our business logic we can try scenarios out in a safe environment implementation in laravel laravel makes it easy to implement domain testing use model factories to create realistic test data that feels real create dedicated test seeders that load the complete scenario in a repeatable way leverage database transactions or refreshdatabase traits for test isolation structure tests around your domain services actions and jobs not just http endpoints your tests become a place where everyone can see the essence of the application laid out in scenarios where changes can be vetted with confidence and where future tools can learn what correct means in your context conclusion domain testing is a practical way to validate business critical behaviour with high confidence if a change breaks fund carry distribution maths a domain test will catch it communicate intent across disciplines through tests written in business language serving as living documentation build on the best of tdd bdd and ddd without reinventing the wheel prepare for ai assisted development by creating tests that define boundaries of correct behaviour and logic as we rediscover testing principles in the age of ai domain testing could be a language of shared understanding between humans software and intelligent agents it ensures that no matter how our code is written by us or by ai the true intent and logic of our business domain remains solid posted on september 7 2025 september 7 2025 categories technology tags laravel icehouse ventures mobile app launch script from the launch of the icehouse ventures mobile application at the annual new zealand startup showcase at spark arena on 21 august 2025 we had some tech issues on the night but it was great to share what we ve been working on with so many people and so far the reaction from users has been great good evening everyone my name is peter thomson and i m the cto at icehouse ventures i have what i think might be one of the best jobs the country because i get to spend my time with all of these great startups we ve heard from tonight and all of you as investors it s like showcase all year round i m particularly grateful to our early adopters many of whom are here tonight like maurice maclaren richard reynolds and ian holland who ve given us such great feedback over the years at icehouse ventures our greatest strength has always been our community of investors founders and operators the great startups coming out of new zealand can become even more unstoppable with a large engaged network backing them but to be engaged you first need to be informed and that s what we ve been working on for the last five years with our custom built investor portal when we started on this journey there wasn t any software available that could connect a large network of investors with a diversified portfolio of startups to deliver an immersive experience while maintaining institutional grade reporting and security it didn t exist so we had to build it but now it s time to take things up a notch we want to supercharge your ability to support these great companies and to do that we re going mobile tonight we re proud to introduce the icehouse ventures mobile app available now on ios and android this new app is your window into the people ideas and companies building the future of new zealand we re putting the brightest new zealand companies in your pocket with a host of new features you ...