OpenAI GPT 5.6 Sol vs Thinking Machines Inkling Small

An API is a set of rules that lets one piece of software ask another for information or actions. For example, a weather app may use an API to request today’s forecast from a weather service, then display the result for you.

An API is a set of rules letting one program ask another for data or actions, like fetching weather from a service. It defines how to ask, what to say, and what answer returns, so different apps communicate without knowing each other's inner workings.

Use a reputable hosted authentication provider. For a two-person startup, authentication is security-critical but rarely differentiating, and building it well requires ongoing work across password storage, account recovery, MFA, session management, abuse prevention, compliance, monitoring, and incident response. A provider lets you launch faster and reduces the chance that a subtle implementation flaw compromises users. Choose one with standards-based integrations and an export path to limit lock-in. The strongest argument against this recommendation is vendor dependency: pricing can rise sharply with user growth, and migrating authentication later can be difficult because password hashes, MFA credentials, sessions, and identity links may not transfer cleanly.

Use a hosted authentication provider immediately. With only two people, your scarcest resource is time, and user authentication is a deep, risky rabbit hole of password security, session management, multi-factor authentication, breach recovery, and compliance obligations that will consume weeks of engineering and expose you to catastrophic liability if done poorly. A hosted service lets you ship today, stay focused on your actual product, and inherit enterprise-grade security without becoming experts in cryptography. The single strongest argument against this is that building in-house eliminates recurring subscription costs, removes vendor lock-in and the risk of a third-party breach or outage affecting your users, and gives you complete customization and data sovereignty over credentials as you scale.

AI spending is hard to predict because usage, pricing, and implementation costs can all change quickly. Unlike traditional software, many AI services charge according to consumption: the number of questions asked, documents processed, images created, or computing resources used. A successful pilot can therefore become much more expensive when thousands of employees or customers adopt it. A second cause is uncertainty about which technology will be needed. Different AI models vary greatly in price, speed, and quality, while vendors frequently release new versions and revise commercial terms. Teams may also discover that a cheaper model is adequate for routine work but that sensitive or complex tasks require more costly systems. Third, the model itself is only part of the bill. Reliable AI often requires data preparation, system integration, security controls, testing, human review, monitoring, training, and legal or compliance work. These supporting costs are easy to underestimate, particularly when projects move from demonstration to production. Executives can improve control by funding AI in stages, with clear limits and decision points. Require each project to define expected usage, business value, accuracy needs, and total operating cost. Track spending by team and use case, set alerts and quotas, and review actual costs monthly. Negotiate vendor protections, avoid unnecessary dependence on one provider, and maintain contingency budgets for rapid growth, compliance changes, or unexpected technical work.

Artificial intelligence budgets defy traditional forecasting because the underlying economics behave more like weather than manufacturing. First, compute and data costs scale unpredictably; a pilot running smoothly on small models can suddenly require tenfold infrastructure investment once production traffic hits, and cloud pricing for specialized GPUs shifts with supply constraints. Second, talent and vendor markets move fast; the consultants who architected last year’s system now command premium rates, while open-source tools adopted can suddenly need expensive commercial licenses to meet security standards. Third, technical obsolescence is relentless; a model family that seemed sufficient six months ago may be superseded by a more capable version, forcing unplanned retraining and integration rather than simple maintenance. To regain control, treat your AI spending as a portfolio rather than a project. Establish monthly governance reviews comparing actual consumption against usage metrics, not just invoices. Build flexible budgets with tiered commitments—commit to fixed costs for core platforms but leave headroom for experimental workloads. Demand transparent pricing from vendors and avoid long-term contracts for rapidly evolving services. Most importantly, tie every dollar to a clear business outcome and sunset initiatives that do not show measurable return within a defined horizon, so unpredictability is contained by strict discipline rather than avoided altogether.