Compare 8 agentic AI software development services firms on production deployments, security and compliance, guardrails, integration depth, seniority, and engagement models.
The Impact of AI on Software Development: What the Data Shows and What It Means for Enterprise Teams
An evidence-based look at the impact of AI on software development, covering productivity research, code security data, role changes, and what enterprise teams should do about it.
Executive Interview Series: Moneesh Arora, CEO of gWorks
At Keyhole Software, we spend a lot of time thinking about what it takes to modernize legacy systems for organizations where the stakes are high and the margin for error is low. Few sectors fit that profile better than local government. We recently sat down with Moneesh Arora, CEO of gWorks, the top-ranked cloud software platform built for municipalities, counties, …
Top Banking Software Development Services (2026)
Compare 8 banking software development firms on documented core banking delivery, regulatory depth, team composition, and U.S.-based senior staffing.
The Shift Toward Local AI: A Developer’s Perspective
AI has moved fast, faster than most of us expected, and the cost of keeping up is starting to show. What began as inexpensive, wide-open access to powerful cloud models has shifted into a landscape of rising token prices, stricter quotas, and unpredictable availability. More teams are starting to ask a question that would have sounded unrealistic a year ago: should we start hosting our AI models locally?
In this post, I’ll walk through why token costs and model access are becoming harder to rely on, why open models and local hardware are closing the gap, and what actually happened when I ran a full agentic workflow on my own machine, starting with the cost problem that kicked this off.