governance Archives

Tech Optimizer

July 23, 2026

Simplify AI agent orchestration with Lakebase Postgres

The auditing process has traditionally been labor-intensive, requiring extensive document reviews. CLA (CliftonLarsonAllen LLP) partnered with the Databricks Forward Deployed Engineering team to develop a document processing application that reduces extraction time from hours to minutes while maintaining quality. The application is built on the Databricks platform, utilizing technologies like Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. The application addresses challenges in document parsing, including unpredictable per-task latency, rate-limit-aware throttling, workload prioritization, cost attribution per task, and real-time progress visibility. It eliminates the need for multiple specialized systems by integrating components such as a web application for user interaction, Lakebase for state management, an orchestrator for task management, and AI agents for document processing. The task queue is supported by two Postgres tables in Lakebase, tracking task status and execution attempts. Concurrency safety is ensured through a locking mechanism, and crash recovery is facilitated by lease-based locking. The orchestrator implements rate-limit-aware throttling and idempotent webhook callbacks to manage task processing effectively. An operator dashboard provides real-time metrics on task status, agent performance, and workload costs, with live updates through Postgres LISTEN/NOTIFY events. Cost attribution is managed at the application level, allowing for detailed monitoring of expenditures. Lakebase enhances the orchestration process with features like autoscaling compute, OAuth-rotated authentication, Unity Catalog integration, and support for branching and snapshots.

AppWizard

July 18, 2026

I used videogames to solve my country’s politics: Turns out the answer is ultra-mega-communism

The Labour prime minister of the United Kingdom has stepped down, leading to the anticipated leadership of former Manchester mayor Andy Burnham. A simulation exercise envisions the UK in 2026 under a radical premiership, proposing initiatives like cryptocurrency, subsidized healthy food, and university grants. The legislation of the right to die significantly boosted approval ratings to approximately 43%. A financial crisis led to a state of emergency, allowing for sweeping reforms such as a state postal service and increased NHS funding. The political landscape is divided, with socialists and trade unionists supporting the leadership, while religious conservatives and capitalists oppose it, forming armed factions. The campaign strategy focused on galvanizing the base, resulting in a 70% turnout from supporters and a nationwide turnout of 72%, translating to 30 million votes and 44.7% of the overall vote for the party.

AppWizard

July 17, 2026

As a PC Gamer Who Hasn’t Bought a Disc in a Decade, I Think PlayStation All-Digital Future Was a Long Time Coming – but There Are Still Reasons To Be Wary

Rockstar has decided not to release a physical version of its upcoming game, GTA 6, while Sony plans to stop producing discs for PlayStation consoles by January 2028. The gaming community has reacted strongly, but it is unlikely that either company will change its decision. The transition to a discless future is seen as inevitable. In the PC gaming sector, physical discs have largely become obsolete, with platforms like Steam dominating the market. The initial skepticism towards Steam has shifted as it became a reliable digital marketplace, leading to a decline in physical media. The absence of physical discs raises concerns about consumer autonomy, pricing, and game preservation, as digital ownership is often limited to licenses rather than full ownership. The shift away from physical media has been ongoing, with modern gaming increasingly reliant on digital downloads and updates.

Tech Optimizer

July 8, 2026

PostgreSQL Extensions Are an Architecture Decision, Not an Afterthought

A common issue in database migrations is the unplanned accumulation of extensions in PostgreSQL, leading to extension sprawl. Teams often install extensions without documenting the rationale, resulting in a complex web of dependencies that complicates future upgrades and removals. The installation process involves PostgreSQL accessing a control file that details the extension's version and dependencies, which can lead to multiple extensions being installed unintentionally. Upgrading and removing extensions are often neglected, causing risks such as the loss of dependent objects. Extensions typically default to the public schema, which can become cluttered; relocating them to dedicated schemas can improve organization. Trusted Language Extensions (TLE) allow non-privileged users to utilize procedural languages in managed databases without needing superuser access. Key extensions recommended for use include pg_stat_statements, pg_trgm, hstore, citext, and PostGIS, each serving specific use cases. Proper governance is essential for managing extensions, including documenting their purpose, ownership, and dependencies, to prevent operational surprises.

Tech Optimizer

July 7, 2026

The TechBeat: How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries (7/6/2026)

- Websites detect VPN usage through factors like IP reputation, DNS leaks, WebRTC, and browser fingerprints, with strategies to mitigate tracking. - Slow Postgres dashboards may be caused by stale metadata statistics rather than the fact table, with solutions provided to rectify these issues. - Spec-driven development enhances the reliability of AI coding agents through structured specifications, workflows, reusable skills, and proof. - A comparison of usage-based billing platforms for 2026 includes Credyt, Metronome, Orb, Lago, Stripe Billing, and Chargebee. - Loop Engineering is a rising trend in AI workflows expected to dominate in 2026, though challenges may accompany it. - ONLYOFFICE released version 3.7 of DocSpace Developer, introducing AI file generation, new webhook events, expanded plugin tools, and enhanced form workflows. - ANY.RUN's data inspection tool offers SOC teams comprehensive browser visibility for quicker phishing investigations. - AI systems are reducing obvious hallucinations, but deeper structural failures remain a risk for professionals. - A vendor audit revealed supply chain vulnerabilities in AI, including unpinned models and the use of trust_remote_code=True. - Developers using OpenAI Codex should avoid overloading a single conversation with tasks to maintain performance, recommending the use of subagents. - Barriers to AI adoption are not purely technological, as shared by insights from discussions with 500 individuals. - Guidance on creating production AI agent loops emphasizes state management, tool governance, evidence verification, and domain-specific controls. - Dawnguard launched a security architecture automation platform with million in pre-seed funding to address AI code vulnerabilities. - The AI boom is driven by complex memory architecture rather than GPUs, influencing consumer electronics pricing strategies. - The origins of functional programming trace back to John McCarthy's practical needs for differentiating expressions. - A guide for AI startup founders addresses common pitfalls in understanding AI agents and the autonomy trap for developing reliable systems.

Tech Optimizer

July 6, 2026

Why most AI projects fail: It’s infrastructure and people

AI technology faces significant criticism for its low success rates in delivering business results, with studies indicating a 95% failure rate for enterprise AI solutions and only 9% of organizations in Europe, the Middle East, and Africa achieving measurable outcomes from AI initiatives. Four main shortcomings hinder the transition of AI prototypes to production: 1. Deployment Flexibility: Prototyping environments often lack the necessary flexibility for large-scale production deployment, particularly in regulated sectors. 2. Data Sovereignty: Production transitions can complicate data sovereignty at enterprise and regional levels. 3. Reliability: High availability is crucial for production environments, but vendor-managed platforms may not guarantee seamless upgrades or hardware swaps without downtime. 4. Disconnect in Tool Selection: Developers often choose tools for prototyping without considering production implications, leading to difficulties in scaling. The shortage of database administrators (DBAs) is exacerbated by the increasing use of AI tools, with 84% of developers utilizing them according to a 2025 survey. To address these challenges, Merrick suggests leveraging AI DBA agents to support human DBAs and improve database management efficiency. He emphasizes the need for both robust data infrastructure and enhanced operational support to improve the success rates of AI prototypes.

Tech Optimizer

July 2, 2026

From monolith to Lakebase to LTAP: rethinking the database from storage up

Sixteen years ago, the author began their PhD at UC Berkeley and was advised to focus on analytics rather than OLTP databases, which were considered solved. This led to the creation of Apache Spark and Databricks. However, during the development of Databricks, it was found that OLTP databases were not solved problems; they were difficult to scale and fragile. This realization led to the development of Lakebase, a serverless Postgres database designed with modern technology. Lakebase architecture separates the write-ahead log (WAL) and data files into independent, scalable services. The WAL is externalized to a service called SafeKeeper, which uses Paxos-based replication for durability, while data files are managed by PageServer, which stores them in cloud object storage. This design addresses challenges such as data loss, scaling, and performance interference between transactional and analytical workloads. Lakebase maintains compatibility with Postgres, offers unlimited storage, serverless compute, durable writes, and simpler high availability. It also introduces LTAP (Lake Transactional/Analytical Processing), which allows both transactional and analytical processing to operate on a single data copy in real time, eliminating the need for separate data copies and reducing costs. LTAP utilizes a unified storage layer that allows data to be materialized in both row and columnar formats, optimizing it for both transactional and analytical workloads. The system ensures that analytics can access the most current data without affecting transactional performance by using a log sequence number (LSN) to retrieve the latest changes. Unlike traditional CDC approaches, LTAP requires no explicit table replication, as all data is stored in a single governed copy. This architecture circumvents common issues faced by hybrid transactional/analytical processing (HTAP) systems, such as incomplete feature sets, lack of ecosystem support, and performance contention. The Lakebase architecture has unlocked capabilities like unlimited storage, elastic compute, durable writes, and instant branching, with ongoing developments anticipated in the future.

Tech Optimizer

July 2, 2026

From monolith to Lakebase to LTAP: rethinking the database from storage up

Sixteen years ago, the author began their PhD at UC Berkeley, where they were advised to focus on analytics rather than OLTP databases, which were considered a solved problem. However, upon developing Databricks, they found that OLTP databases were cumbersome, challenging to scale, and fragile. This led to the creation of Lakebase, a serverless Postgres database designed to address these issues by externalizing the write-ahead log (WAL) and data files into independent services. Lakebase architecture separates the WAL into a distributed service called SafeKeeper and data files into another service called PageServer, allowing for improved durability, scalability, and performance. This architecture eliminates data loss risks associated with disk failures and misconfigurations, simplifies high availability, and allows for elastic compute resources. Lakebase also introduces LTAP (Lake Transactional/Analytical Processing), which enables transactions and analytics to operate on a single data copy in real-time, avoiding the need for separate data copies and reducing costs. The system retains Postgres's ACID semantics while storing data in open columnar formats, making it accessible to both transactional and analytical engines without the delays and complexities of traditional replication methods. The architecture allows for unlimited storage, serverless compute, and instant branching, while ensuring that analytics can read the most current transactional data without affecting transactional workloads. LTAP aims to unify transactional and analytical processing by leveraging distinct compute engines for each workload while integrating at the storage layer.

Tech Optimizer

July 2, 2026

EDB Named a Leader in Multimodel Data Platforms Evaluation

EDB has been recognized as a Leader in The Forrester Wave: Multimodel Data Platforms, Q2 2026, with EDB Postgres AI (EDB PG AI) achieving the highest scores in Vision, Innovation, Roadmap, and Partner Ecosystem criteria. EDB PG AI integrates transactional, analytical, and AI workloads into a unified platform, supporting open-source frameworks and enabling various deployment options. The platform features governance at the data layer and is designed for operational efficiency, allowing organizations to implement sovereign AI quickly. EDB PG AI can be deployed on-premises, in hybrid environments, or across cloud infrastructures, backed by partnerships with companies like Dell, IBM, and NVIDIA.

Tech Optimizer

July 2, 2026

EDB named Forrester Leader in multimodel data platforms

EDB has been recognized as a Leader in Forrester's Multimodel Data Platforms evaluation for Q2 2026 for its EDB Postgres AI platform, receiving the highest scores in Vision, Innovation, Roadmap, and Partner Ecosystem. The platform is designed to manage mixed translytical and AI workload demands, offering flexibility in deployment across on-premises, hybrid, and multi-cloud environments. EDB's recent product update introduced agentic database and converged analytics functionalities, reportedly accelerating database tuning by up to tenfold and reducing analytics ownership costs by as much as 58%. The platform is supported by a partner ecosystem that includes Dell, IBM, NVIDIA, Red Hat, and Supermicro, which plays a crucial role in influencing database purchasing decisions. EDB's roadmap focuses on advancements in GPU-accelerated workloads, semantic intelligence, governance, and knowledge graph functionalities. The emphasis on sovereign deployment aligns with organizations' needs for control over sensitive data amidst stricter regulations.