How to Build a Storage Engine That Cryptographically Proves Data Integrity

·1 views

Most traditional databases focus on reading, writing, and crash recovery, but they cannot prove that stored data has not been silently altered. A tamper-evident storage engine addresses this gap by cryptographically linking each committed record to all previous ones, forming a hash chain where any modification breaks the sequence and becomes detectable. The architecture relies on an append-only journal, a hash chain, and periodic signed Merkle checkpoints that serve as external trust anchors. Without external checkpoints, a sufficiently privileged attacker could rewrite the entire local chain and recompute all hashes, making the tampered data appear consistent. This design is particularly relevant for financial records, audit trails, compliance systems, security logs, and software supply-chain metadata.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Five Questions to Ask Before Building on a Free AI Platform Trial

Engineering teams often integrate free AI platform tiers into their workflows without fully understanding the trade-offs, only to face compliance and reliability issues later. A decision framework built around five key questions helps teams evaluate whether to use a managed free-tier AI platform or self-host their own stack. The five areas of scrutiny are data boundaries, latency tolerance, load patterns, operational responsibilities, and exit costs. Each gate requires evidence-based answers rather than assumptions, with particular emphasis on testing vendor lock-in risks from day one. The framework uses MonkeyCode, an open-source AI development platform with a free server option and a 10 million token allowance, as a worked example.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Build Real-Time Inventory APIs That Prevent Overselling and Stock Errors

Inventory management systems often return incorrect stock quantities when multiple operations update the same SKU simultaneously, causing overselling or negative stock. A reliable architecture requires explicit concurrency rules, transaction boundaries, idempotency, and a full event audit trail rather than simple CRUD endpoints. One practical approach combines Node.js, PostgreSQL, Redis, and AWS, where PostgreSQL serves as the authoritative source of truth for all stock-changing transactions. Instead of a read-then-write pattern, stock mutations should be validated and applied within a single atomic database transaction to eliminate race conditions. An inventory ledger table recording every quantity change alongside the main inventory table provides both operational accuracy and a complete audit history.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Cybersecurity Learner Documents Hands-On Nmap Network Reconnaissance Study

A cybersecurity self-learner published a write-up in August 2026 documenting their practical experience with network reconnaissance using Nmap. The study was conducted as part of a structured self-education effort being pursued alongside an active job search. The learner worked through guided rooms covering Nmap's core scanning capabilities, gaining firsthand experience identifying open ports and running services on target systems. The exercise highlighted the difference between theoretical knowledge and practical application of reconnaissance tools. The author indicated plans to build on this foundation in subsequent study sessions.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Treat AI Models as Dependencies, Not Products, to Avoid Integration Failures

A software team recently lost three days of work after switching LLM providers over a weekend, only to discover a schema mismatch where the new provider returned responses in a different format than the old one. No tests had been written against the response contract, causing every prompt template in the codebase to return empty strings. The author argues that the real product teams ship is the contract between their app and the model, not the model itself, and that it should be defined before a model is even chosen. To enforce this, the author builds a lightweight API gateway that accepts a fixed request shape, calls a configurable provider, and always returns a fixed response shape, with model names stored as environment variables. Running a contract test suite against this gateway in CI can prevent the kind of costly provider-swap failures that stem from undocumented response format assumptions.

0 comments Read more at DEV Community