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How to choose a software development partner

Most of the cost of building software is not the code. It is choosing who builds it with you, and getting that decision right before the first line is written.

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What is a software development partner?

Compare your options

In-house vs outsourced development: which fits your product?Development agency vs staff augmentation Onshore, nearshore, or offshore development

Vet a partner

Questions to ask a software development partner before you hire How to evaluate a development team's engineering quality How to run a paid trial project before you commit

Work together well

How to scope the first project with a new partner Software development pricing and engagement models How to work well with an embedded development team

How to build an AI product that reaches production

The hard part of an AI product is not the demo. It is everything between a demo that impresses and a product people trust every day. This guide is about crossing that gap.

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AI product vs AI feature: which are you building?

Idea to prototype

How to validate an AI product idea before you build How to prototype an AI product in weeks

Demo to production

The AI last mile: from demo to production How to evaluate AI quality with real measurement Reducing AI hallucinations and handling errors

Build decisions

Build vs buy for AI: train a model or use one?How to choose an AI model for your product What it costs to build an AI product Who you need to build an AI product

How to launch a product: from idea to MVP

An MVP is not a smaller version of the product you dream about. It is the fastest honest test of whether the product is worth building at all. Get that framing right and everything after it (scope, stack, team, launch) gets simpler and cheaper.

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What is an MVP (and what it is not)

Decide what to build

How to validate a product idea before you build How to scope an MVP How to prioritize features for a first version

Build it

How long it takes to build an MVP Building an MVP with a small team Choosing a tech stack for an MVP

After launch

Getting your first users and feedback When to scale past the MVP

Technical due diligence: a complete guide for investors

Before you write the check, you need to know whether the technology can carry the plan you are paying for. Technical due diligence is how you find that out, in the weeks you have, without becoming an engineer yourself.

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What is technical due diligence?

What to assess

How to assess code quality Assessing architecture and scalability Assessing the engineering team Assessing security and compliance Assessing technical debt

The process

A technical due diligence checklist How to run technical due diligence Technical due diligence red flags

How to scale your engineering team

Scaling an engineering team is not about hiring faster. It is about adding the right capacity at the right time, in a way that makes the team quicker rather than slower. Most teams get the timing and the method wrong, and pay for it in speed.

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When to scale your engineering team

Ways to add capacity

How to hire senior engineers Staff augmentation, explained Building a remote engineering team

Keep quality high

How to measure engineering productivity How to structure engineering teams How to onboard engineers quickly

Pitfalls

Why adding engineers can slow you down How to reduce coordination overhead

Custom software: build vs buy, done right

Most companies do not need custom software for everything. They need it for the few things that make them different, and they need to buy the rest. This guide is about telling those apart, then building the custom part in a way you will not regret.

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What is custom software?

The decision

Build vs buy software: how to decide When off-the-shelf software fails you Total cost of ownership for custom software

Build it well

How to write software requirements How to choose a technology stack How to avoid custom software failure

Own it long term

Maintaining custom software How to avoid vendor lock-in

What software costs and how to budget it

The price of software is not one number. It is the result of choices you make about scope, seniority, and how right the product has to be. This guide explains what actually drives cost so you can build a budget that holds, instead of chasing a quote that does not.

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What drives software development cost

Cost by project type

What it costs to build an MVP What it costs to build a web application What it costs to build a mobile app

Pricing and models

Fixed price vs time and materials In-house vs outsourcing cost

Control the cost

How to reduce software development cost The hidden costs of software development

From AI-generated code to production software

AI can write most of a codebase in an afternoon. It cannot tell you whether that codebase is safe to run. This guide is about the gap between the two, and how to close it.

Understanding the risk

What is AI-native software development?The real risks of taking vibe-coded software to production Is AI-generated code secure? What the testing shows

Fixing and reviewing

How to review AI-generated code before it ships What it takes to fix a vibe-coded app Can your team maintain AI-written software?

Adopting the practice

How to move an existing engineering team to AI-native development

Choosing who helps you

AI coding governance: what enterprise teams need in place How to evaluate an AI development partner

Eval-driven development: how to prove AI-written code works

AI can write a feature in minutes. Proving that the feature does what you asked still takes real work, and that work is now the only thing standing between a fast team and a broken product.

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What is eval-driven development?The verification gap: why AI made writing code cheap and checking it expensive Evals vs tests vs code review: which one catches what

Build the eval suite

How to write your first eval suite What to check in an eval suite: the seven things that matter Writing specs an AI agent can verify Using a model as a judge, without fooling yourself

Run it continuously

Running evals in CI when agents write the code Metrics for AI code quality: what to watch instead of volume When evals give false confidence Agent debt: when generation outpaces review

Roll it out

Adding evals to a codebase that has none The business case for evals

How to deliver high-quality software with AI-native development

Generation is no longer the constraint. Everything around it is: deciding what to build, proving it works, releasing it safely, and running it afterwards. This guide is the operating model for the team that has to do all four, at the speed AI now makes possible.

Set the standard before you build

What done means when an agent writes the code How to break work into units an agent can finish The decisions that stay with your engineers

Gates that hold at speed

The quality gates that should block a merge Test strategy when the AI writes the tests too Dependency control when an agent picks the packages

Release and operate

Releasing AI-written changes: flags, canaries and rollback What to instrument when nobody typed the code Incident response when nobody remembers writing it

Keep the codebase liveable

Keeping one codebase when everything is generated Documentation an agent and a new hire can both use Refactoring with agents without changing behaviour

Run the team and the numbers

Measure delivery, not how much code was generated Team shape and ownership when agents do the typing Budgeting agent capacity and cost

Taking AI agents from prototype to production

An agent that works in a demo and an agent that works in production are different systems. The demo has to succeed once, with someone friendly at the keyboard. Production has to succeed repeatedly, for people who did not read the instructions, with real money and real data on the other side of the call.

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What an AI agent actually is, and when you need one Why AI agent pilots stall before production

Make it reliable

How to evaluate an AI agent before you trust it How to make an AI agent reliable enough to ship

Run it safely

Permissions and guardrails for AI agents Single agent or multi-agent: choosing an architecture

Plan the work

What it costs and how long it takes to ship an AI agent

Product discovery and UX design: a buyer's guide

Most failed software was built competently. The team shipped what was asked for, on a reasonable timeline, and it still did not matter, because what was asked for was never tested against what users actually needed. Discovery and design are how you buy that test before you pay for the build.

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What product discovery actually delivers The signs your product has a UX problem

Hire the right help

How to choose a product design agency Design agency vs freelancer vs in-house designer

Run the work

How much UX research is enough?How to brief a design partner

Own the result

Design systems: when to invest, and when not yet Design handoff without losing the product

Building compliant software in financial services

Financial regulation is already written as rules. That is the part most teams miss. A rule that says a record must be recreatable after deletion is a specification, and a specification can be turned into a check that runs on every change. This guide covers how to do that, which obligations actually reach your code, and why compliance gaps in a working app are so easy to miss.

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Why AI-native development fits regulated work How to translate a regulation into engineering requirements

The rules that reach your code

SEC recordkeeping requirements in your application The FTC Safeguards Rule for product teams Audit trails that satisfy an examiner

Building it right

Using evals as compliance evidence Access control and least privilege in financial applications Change management and release controls that hold What SOC 2 actually asks of your engineering process

Auditing what you have

The compliance gaps teams miss in their own app How to audit an existing financial application

Building compliant software in healthcare

HIPAA is unusual among regulations in that it tells you what to achieve and mostly declines to tell you how. That flexibility is why so many healthcare applications are non-compliant while holding a completed risk assessment: the rule left the decision to you, and nobody wrote the decision down. This guide covers what the Security Rule requires from software, and how to turn it into something a system enforces.

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What HIPAA actually requires from your software Required vs Addressable safeguards, and why it matters

The rules that reach your code

Audit controls under the Security Rule Minimum necessary in a permission model Encryption and the proposed Security Rule update

Building it right

Business associates and third-party services Building AI features on clinical data

Auditing what you have

Where PHI leaks in a working application

Building compliant software in real estate

Real estate software has a compliance problem the other regulated industries do not. In finance and healthcare, the rules mostly govern how you handle data. In housing, the rules govern the decision your software makes, which means the model's output is itself the regulated act. That changes what you have to build and what you have to test.

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What regulation reaches a real estate product Why removing the protected field is not a defence

The rules that reach your code

AVM quality control standards in practice Tenant screening and fair housing Housing advertising and audience targeting

Building it right

Testing a model for disparate impact Explainability and adverse action

Auditing what you have

Compliance gaps in proptech products

Building compliant software in insurance

Insurance regulation is state by state, which most teams treat as a reason to guess. It is really a reason to build the rule into configuration rather than into code, so the same application can satisfy Iowa and Ohio without a special case for each. This guide covers what the NAIC model laws actually require, which parts reach your application, and why a working system so often looks compliant from the inside while missing the one thing an examiner asks for.

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Why insurance software needs a compliance-first build Translating NAIC model law into engineering requirements

The rules that reach your code

State-by-state data security requirements for insurance software Incident response and breach notification in your application Model Audit Rule controls for software teams

Building it right

Evals as insurance compliance evidence Designated security officer and access control

Auditing what you have

Compliance gaps in insurance software

Building compliant software in legal technology

Legal technology carries a rule most other software does not: the product itself can cross a line into practicing law, even when nobody intended it to. This guide covers where that line sits, what the ethics rules that govern lawyers actually require of the tools they use, what e-discovery obligations reach your data model, and how to turn each of those into something a build can be tested against rather than a policy nobody rereads.

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Why legal software needs a compliance-first build Avoiding unauthorized practice of law in product design

The rules that reach your code

ABA Rule 1.6 confidentiality controls for legal software The technology competence duty and what it means for vendors E-discovery and retention requirements under FRCP

Building it right

Evals as legal compliance evidence AI drafting features and confidentiality risk

Auditing what you have

Compliance gaps in legal software

Software for compliance-heavy industries

Regulated software has always needed exhaustive verification and rarely received it, because writing the checks cost more than anyone would approve. That constraint is gone. This guide covers what that changes, the obligations that apply regardless of sector, and how to tell a partner who verifies their work from one who says they do.

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Compliance is a specification problem

Rules that cross every sector

The EU AI Act and your product Retention and deletion across every copy

Building it right

Building an audit-ready codebase When a human must stay in the loop

Choosing a partner

Choosing a development partner for regulated work

AI News, page 8 of 8

AI and software engineering, as it happens.

restoredrill restores your Postgres backup into a throwaway container and writes down that it worked

restoredrill fetches your latest PostgreSQL backup, restores it into a disposable container, runs checks you define, and writes a JSON report with a real timestamp and the restore time. MIT, written in Go.

Infrastructure

RealDiff runs your tests on both sides of a pull request and diffs what the code actually did

RealDiff builds both branches with runtime instrumentation, runs the same test suite on each, records every method call's arguments and return values, and diffs the two traces instead of reading the source diff.

Dev tools

IndexFlow is search indexing infrastructure as Rust crates you run yourself

Sitemap processing, technical validation, IndexNow submission and multi-site scheduling, published as Rust crates under MIT or Apache 2.0 with a rolling quota circuit breaker.

Open source

oMLX writes the KV cache to SSD, so a coding agent on a Mac stops recomputing the whole context

A local inference server for Apple Silicon that persists KV cache blocks across an in-memory tier and an SSD tier, so a shifting prompt prefix does not force a full recomputation. 21,152 stars, Apache 2.0.

Infrastructure

Superagent gives every chat its own git checkout, and lets the agent drive a browser you can watch

A macOS desktop app for Claude Code where every conversation gets a private branch and checkout, the agent drives a visible browser on your own logins, and an iOS Simulator streams into the window.

Dev tools

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