Andrew Avery
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Products by Andrew
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Vialmetry: Peptide Calculator
Health-fitness
For people managing their own injection routines, the math and logistics can quickly become error-prone. Converting a vial's concentration and the volume of diluent added into syringe units, tracking which injection site was used most recently, and maintaining a coherent record across notes apps and spreadsheets creates friction that compounds with every dose. Vialmetry addresses this by consolidating the entire workflow into a single logbook designed specifically for this purpose. The app keeps track of vials you prepare—recording the strength, water added, mix date, and remaining volume—and calculates the amount of solution needed based on your target dose. Rather than hiding these calculations, Vialmetry displays the arithmetic behind every number, letting users verify the working themselves. This transparency is uncommon in health-tracking software and signals a product built for people who want to understand, not just execute. The feature set reflects this precision-focused ethos. A body map overlays thirty injection sites across front and back views, automatically rotating based on a user-defined rest period, removing the guesswork from site selection. The app supports custom schedules with reminders that can remain silent on the lock screen, and it logs every injection with its corresponding site, dose, and cost. Historical entries can be corrected or deleted, with the vial count adjusting accordingly. For clinical coordination, users can print summaries or export to CSV. The privacy model is notably strict: no account creation required, no ads, no tracking, and full offline operation. An optional cloud backup exists but only syncs when the user initiates it, and Face ID or fingerprint locks provide device-level security. This design avoids the creep of data collection common in health apps. Vialmetry explicitly disclaims being a medical device and states it never suggests doses, compounds, or schedules—functions reserved for clinicians. The formulas and methodology are published publicly, underscoring the approach. The app is free across iOS and Android, removing cost as a barrier to adoption. The product targets a specific, self-aware audience: people already managing their own regimen who need better tools for the arithmetic and record-keeping, not people seeking medical guidance.
PromptSpend
Prompt-engineering-tools
Every cost comparison tool becomes obsolete the moment vendors adjust their pricing. PromptSpend addresses this fundamental problem by treating the pricing pipeline itself as the core product rather than treating price data as static information to collect once. The calculator supports 78 LLM models including GPT and Claude, allowing side-by-side comparison of up to four models at a time. Users can input real prompts or specify their usage scale to see monthly cost projections. The platform targets developers and technical teams making model selection decisions. What distinguishes PromptSpend from existing calculators is its automated approach to freshness. The system re-checks pricing information every morning against vendor pages, flagging discrepancies instead of simply averaging different sources. Each price quote includes the source URL and confirmation date, providing verifiable attribution that existing calculators typically omit. The tool also addresses two cost factors that most calculators ignore or get wrong. The first is compounding chat history—each message in a conversation includes previous messages, yet calculators often treat input costs as linear. The second is hidden reasoning tokens, which affect models that perform internal reasoning steps before generating output. These technical details significantly impact real-world costs at scale. PromptSpend offers three access points beyond the web calculator. A public API lets developers integrate pricing lookups programmatically. An MCP server makes the tool available to coding agents, enabling AI assistants to check costs while writing code. A VS Code extension shows pricing information inline on the line of code that calls a model. The pricing model is transparent: the tool is free, requires no account to use, and operates under an open source license. The founder chose this model deliberately, framing the continuously-updated pricing data as the key value proposition. Rather than monetizing access to price information or user data, the focus remains on the open source infrastructure itself being valuable to the ecosystem. For teams running multiple LLM models in production or evaluating model choices, the combination of automated pricing accuracy and technical cost details addresses real friction in AI infrastructure decisions.