Sandboxing: Foolproof Boundaries vs. Unbounded Foolishness
Sandboxing mitigates the risks of software so large and complex that it's likely to harbor security vulnerabilities. To safely harness useful yet ominously opaque libraries, a simple mechanism provides ironclad confinement—or does it?
Peer Mentoring
Stop waiting for a senior mentor to appear. Your peers are some of the most valuable mentors you'll ever find. Start leveraging those relationships, sharing insights, and bringing value to every conversation. Your career will thank you for it.
Can't We Have Nice Things?
We build apparatus in order to show some effect we're trying to discover or measure. A good example is Faraday's motor experiment, which showed the interaction between electricity and magnetism. The apparatus has several components, but the main feature is that it makes visible an invisible force: electromagnetism. Faraday clearly had a hypothesis about the interaction between electricity and magnetism, and all science starts from a hypothesis. The next step was to show, through experiment, an effect that proved or disproved the hypothesis. This is how empiricists operate. They have a hunch, build an apparatus, run an experiment, refine the hunch, and then wash, rinse, and repeat.
Generative AI at the Edge: Challenges and Opportunities:
The next phase in AI deployment
Generative AI at the edge is the next phase in AI's deployment: from centralized supercomputers to ubiquitous assistants and creators operating alongside humans. The challenges are significant but so are the opportunities for personalization, privacy, and innovation. By tackling the technical hurdles and establishing new frameworks (conceptual and infrastructural), we can ensure this transition is successful and beneficial.
Develop, Deploy, Operate
By taking a holistic view of the commercial software-development process, we have identified tensions between various factors and where changes in one phase, or to infrastructure, affect other phases. We have distinguished four distinct forms of impact, warned against measuring against unknown counterfactuals, and suggested a consensus mechanism for estimating DDR (defect detection and resolution) costs. Our approach balances product outcomes and the strategic need for change with both the human and machine costs of producing valuable software. With this model, the process of commercial software development could become more comprehensible across roles and levels and therefore more easily improved within an organization.
AI: It's All About Inference Now:
Model inference has become the critical driver for model performance.
As the scaling of pretraining is reaching a plateau of diminishing returns, model inference is quickly becoming an important driver for model performance. Today, test-time compute scaling offers a new, exciting avenue to increase model performance beyond what can be achieved with training, and test-time compute techniques cover a fertile area for many more breakthroughs in AI. Innovations using ensemble methods, iterative refinement, repeated sampling, retrieval augmentation, chain-of-thought reasoning, search, and agentic ensembles are already yielding improvements in model quality performance and offer additional opportunities for future growth.
The Point is Addressing:
A brief tour of efforts to reimagine programming in a world of changing memories
Even something as innocent as addressing comes from a rich design space filled with tradeoffs between important considerations such as scaling, transparency, overhead, and programmer control. These tradeoffs are just some of the examples of the many challenges facing programmers today, especially as we drive our applications to larger scales. The way we refer to and address data matters, with reasons ranging from speed to complexity to consistency, and can have unexpected effects down the line if we do not carefully consider how we talk about and refer to data at large.