Matt Asay

Fueled by vibes and with stars in their eyes, enterprises are not taking the time to understand genAI’s limitations and to create their own rules-based approach.

Considering how much we depend on open source, we should worry about the financial health of our most critical projects.

Overspending on AI infrastructure by cloud providers has some forecasting an AI bust, but there are signs that enterprises are starting to put AI to work.

The schoolyard chaos at OpenAI could detract from corporate adoption of AI. Microsoft may be best suited to restore order and trust.

Take a look at this unscientific survey to discover some ways to keep developers happy. A few are even simple.

Without skilled developers supervising AI coding assistants, they are likely to break your code rather than write it. Right now, only people can fine-tune and evaluate AI.

Yes, a tiny number of companies have relicensed their open source code. Let’s worry about actual problems, like security and megacompanies that contribute almost nothing.

Apache Airflow is a great data pipeline as code, but having most of its contributors work for Astronomer is another example of a problem with open source.

The inherent weaknesses of large language models are reason enough to explore other technologies, such as reinforcement learning or recurrent neural networks.

The Redis license change hurts no one except AWS and other cloud giants. If they gave back a little, they wouldn’t need watered-down forks.

When the Open Source Definition isn’t applied to cloud-distributed software, and the General Public License allows cloud companies to grow rich without contributing much, everyone loses.

Is the complexity of billing better handled by buying software or building it? Lago offers developers a chance to get back to solving core business problems.

AI coding tools such as Amazon CodeWhisperer and GitHub Copilot boost the productivity of pro and novice developers alike. They’re only the beginning.

Software freedom. User freedom. Why do we have to choose? Licensing hasn’t kept pace with the reality of software in the cloud and AI.

Once you’ve gotten settled in the cloud, figure out where to innovate, and look for a unicorn vendor that can make AI simple and effective.

With so much to figure out with AI, it makes sense to simplify with familiar development techniques, especially infrastructure as code and Python.

AI-generated code has transformed software development forever. That’s not necessarily good. A solid review process can shrink bloat and attack surfaces.

Pour les développeurs, disposer d’une bonne documentation est essentiel. Mais le constat vaut aussi pour les assistants basés sur l’IA générative.

Data is the heart of the user experience, so shouldn’t developers start there? SQLite, NoSQL databases, and abstractions like Neurelo make that far easier to do.

GraphQL gives developers a flexible and unified way to connect data and services. Its query planning and policy engine make it a promising option for adding LLMs to the mix.