Farzan Ansari
Farzan owns technical direction, architecture, integrations and deployment at Kastling. He works across AWS and Azure, and designs the approval points that keep a named person in control of consequential decisions.
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Using Claude and ChatGPT for security audits and red teaming: what worksClaude and ChatGPT are useful first-pass reviewers of your own code, configurations and AI features, and they are weak as a final authority. Use them to find common flaws, explain findings, draft threat models and triage scanner output, keep Semgrep or CodeQL running beside them, and have a person confirm every finding. For regulated systems, an AI pass does not replace an independent penetration test.Read The encryption techniques that matter, and what the industry uses by defaultThe types of encryption that matter in practice are AES-256-GCM for stored data, TLS 1.3 for traffic, envelope encryption with a cloud key service for data at rest, Argon2id hashing for passwords, and hybrid post-quantum key exchange, which browsers already use. This guide is for operations, IT and finance leaders who need to know the current default for each job and the questions to put to a vendor. Never invent your own cryptography.Read How to build a system that handles hundreds of millions of data pointsA high volume data pipeline works in stages: a durable ingestion buffer, stream processing, raw data kept cheaply in object storage as the source of truth, and pre-aggregated rollups that answer the questions people actually ask. The design choice that matters most is deciding those questions first, because they set what you aggregate and how long you keep detail. This guide is for operations and IT leaders at companies of roughly 20 to 200 people who collect sensor, image or event data at volume.Read The history of large language models: a dated timeline and what it means for buyersLarge language models grew out of statistical word prediction, gained their modern shape with the transformer in 2017, and reached the public with ChatGPT in November 2022. The history teaches four things to a business buyer: capability per dollar changes fast, model rankings keep reshuffling, training method matters as much as size, and no single vendor should be a permanent dependency. This guide is for leaders at companies of roughly 20 to 200 people.Read How do LLMs work? A plain-English guide for people who buy and run themA large language model (LLM) reads text as tokens, turns them into numbers, and uses attention to predict the next token, over and over, until a reply is complete. It generates likely text instead of looking facts up, which is why you test it against your own work, ground it in your documents, and keep a person approving consequential actions. This guide is for operations and IT leaders at companies of roughly 20 to 200 people.Read How Meta handles billions of requests, and what your company should copyMeta (formerly Facebook) serves its social graph through layers: load balancers, a very large cache, and a sharded MySQL tier behind it. A company of 20 to 200 people should copy the order of operations, which is to measure, index, cache, add read replicas, partition, and shard last, because most never need sharding. This guide explains each technique and when it starts paying off.Read Machine learning vs deep learning: which one fits your business data?Deep learning is a kind of machine learning that uses many-layered neural networks, and it wins on images, audio and language. For spreadsheet-shaped business data such as fraud flags, credit, churn and demand forecasts, start with classical machine learning, usually gradient-boosted trees. For language and documents, try a pretrained model through an API before training anything. This guide is for operations, finance and IT leaders at companies of roughly 20 to 200 people.Read When to use pub/sub, a queue or Kafka: a plain guide for growing businessesUse pub/sub when one event must reach several systems, a queue when each task should go to one worker, with retries and a handler that tolerates the occasional duplicate, and a direct API call when the caller needs the answer now. Most businesses of 20 to 200 people should start with a managed queue or topic on AWS or Azure, and move to a Kafka-style log only when several consumers must replay history at their own pace or event volume grows past what a queue handles comfortably.Read Rust vs TypeScript: when to use eachTypeScript is the right default for web applications, APIs and business software because of its hiring pool, speed of change and ecosystem. Rust earns its higher cost in CPU-heavy paths, latency-critical services, embedded and edge work, and anywhere memory safety and predictable performance matter. The two are often combined, and this guide is for owners and technical leads at companies of 20 to 200 people deciding what to build in.Read Sentry vs PostHog: which one does your team need?Sentry is built for developers who find and fix errors and performance problems, and PostHog is built for product teams who need to understand user behavior through analytics, session replay, feature flags and experiments. Each has moved into the other's territory: PostHog now offers error tracking and Sentry offers session replay. This guide is for technical leads and product owners choosing monitoring for a web application, and for small teams deciding whether one tool is enough.Read SQL vs NoSQL: which database should a business application start with?Most business applications should start on a relational (SQL) database, usually PostgreSQL or MySQL, and add a NoSQL system only for a specific workload that needs it. Two old beliefs no longer hold: NoSQL products now offer transactions, and relational databases scale much further than their reputation suggests. This guide is for owners and operations leaders at companies of roughly 20 to 200 people who are approving a database choice.Read What is system design? The questions to ask before choosing an architectureSystem design is deciding a system's components, how data flows between them and which tradeoffs you accept, before anyone builds it. Ten questions on scale, data, uptime targets, integration, failure, security, team skills, cost, change and reversibility settle most of the choices. For a small team, the usual verdict is one well-organized application on one database, split into services only when a measured reason appears.Read Types of data analysis: what each one answers and which to start withThe four core types of data analysis are descriptive (what happened), diagnostic (why), predictive (what will happen) and prescriptive (what to do), with exploratory, inferential and causal analysis as three supporting methods. Most companies of roughly 20 to 200 people get more value from reliable descriptive and diagnostic reporting than from predictive models, so build those first. This guide is for finance, operations, sales and supply chain leaders.Read Types of databases: what each one is for, and which ones a business needsThere are nine common types of databases: relational, document, key-value, wide-column, graph, time-series, search, vector and columnar analytical. Each solves one kind of problem well, but most companies of 20 to 200 people need one relational database, often PostgreSQL, and a cache or a reporting store only when a measured problem appears. This guide is for business and technical leaders deciding what to add to their stack.Read How Vercel runs modern frameworks, and when to choose it over AWS or AzureVercel suits web front ends and framework applications owned by a small team, because it derives the infrastructure from your code and bills for active compute. AWS or Azure suits work that needs private networking, a specific compliance posture, long-running or GPU workloads, or integration with an existing cloud estate, and many setups use both. This guide is based on Vercel's documentation as of October 2026 and is written for technical and operations leaders choosing where to host a business web application.Read 5 things to check before you ship a vibe-coded web or mobile appVibe coding is fine for prototypes and low-stakes internal tools, and risky once real customer or employee data, logins or money are involved. Before you ship, check five things: access rules and database row-level security, secrets in client code, dependencies nobody read, what the agent can do in production, and who owns the app after launch. This guide is for business owners and small teams building a web or mobile app with a coding agent.Read Webhooks vs polling: how to connect business systems without missing eventsUse webhooks when you need to react to an event within seconds, and polling when the source system offers no webhooks, you cannot expose a public endpoint, or a delay of minutes is acceptable. For anything that touches money or inventory, run both: webhooks for speed and a scheduled reconciliation poll to catch what the webhooks missed. This guide is for operations and IT leads at companies of roughly 20 to 200 people who are connecting an ERP, CRM or ecommerce platform.Read WebSockets vs SSE vs WebRTC: how to choose real-time updates for a business appUse server-sent events (SSE) when the server only needs to push updates to the screen, WebSockets when the browser and server both send messages continuously, and WebRTC for live audio and video between people. For most internal tools and portals, SSE or plain polling is enough, and WebSockets earn their extra moving parts only for chat, collaborative editing and two-way live data. This guide is for product owners and technical leads at companies of roughly 20 to 200 people.Read What is WebRTC? How real-time voice and video work, and when to buy it instead of building itWebRTC is the open set of web standards that lets browsers and apps send live audio, video and data to each other with very short delay, encrypted by default. Businesses meet it in video support, in-browser phone lines and voice AI agents. Buy it through a managed provider unless you have a team to run relay and media servers. Written for technical leads and the operations leaders who sponsor them, at companies of roughly 20 to 200 people.Read What separates a good developer from a bad one now that agents write the codeTyping code has become cheap, so the differences between developers now sit in judgment: framing the problem, designing the system, reviewing and testing what an agent produces, and owning the result in production. Good developers ask precise questions and verify the answers, while weak ones accept whatever compiles. This guide is for CTOs, IT leads and founders who hire or manage developers.Read Why Postgres became the default database, and is it really faster?Postgres became the default because it is reliable, extensible and free, not because it is faster than every other SQL database. No engine is fastest across all workloads, and MySQL, SQL Server or Oracle can win for specific ones. This guide explains where Postgres came from, what makes it strong, and where it is weak, for leaders choosing the database behind a business application.Read Advantages and disadvantages of AI workflow automation for mid-size businessesAI workflow automation cuts time on repetitive, document-heavy work, but pays off only when the process is stable, the data is usable and a named person owns the exceptions. This guide weighs the benefits and risks for decision-makers at companies of roughly 20 to 200 people, with the condition behind each advantage and the mitigation for each disadvantage. It also covers when not to automate and how human approval changes the risk.Read How to write an AI acceptable use policy for employeesAn AI acceptable use policy tells employees which AI tools they may use, what data may go into them, how closely to check the output, and who to ask when a new tool shows up. This guide for companies of roughly 20 to 200 people includes a sample policy you can copy, a plan-by-plan comparison of vendor data terms and a rollout plan. A workable policy fits on two pages, matches its rules to how sensitive the data is, and names one owner who keeps it current.Read AI agents with human approval: how to keep a person in controlAI agents with human approval means deciding in advance which actions a person must see before they happen, and how much of the work still reaches them. Money, legal terms, customer messages and irreversible changes need a person by default, while routine, reversible work can run on sampled review instead. Get the design wrong and you either slow the agent to a crawl or approve items nobody actually checked, so this guide for operations and IT leaders at companies of roughly 20 to 200 people shows how to design it.Read AI agents vs RPA vs workflow automation: which fits your process?Workflow automation connects systems through APIs, RPA drives an existing screen when no API exists, and an AI agent interprets a request and chooses its own steps. Pick between them based on what the systems allow and how much of the work follows a written rule, not on which sounds newest. This guide for operations and IT leaders at companies of roughly 20 to 200 people includes a comparison table and a decision guide, because most working processes combine a workflow, an AI step and a person approving anything consequential.Read AI automation agency: when to hire one, and when not toAn AI automation agency or implementation partner is the right choice when the workflow crosses several systems, involves documents or judgment calls, and your team has no spare engineering capacity. A no-code tool, a freelancer or a vendor services team is often better for a single, simple task, and an in-house hire fits once there is steady work. Whoever builds it, the code, accounts and documentation should end up in your company name.Read AI data security and governance for mid-size companies: a practical checklistAI data security and governance means knowing which data each AI workflow touches, approving the sources it may use, checking what your AI provider does with prompts and outputs, and making the AI respect the same permissions as the person using it. This guide for companies of roughly 20 to 200 people also covers defenses against prompt injection, logging with sensible retention and human approval for consequential actions, with frameworks such as the NIST AI RMF and ISO/IEC 42001 giving that work a structure.Read AI document processing for invoices, contracts and formsAI document processing combines three techniques: OCR to turn images into text, layout models to understand where values sit on a page, and language models to extract and interpret fields that vary between documents. The technique matters less than what surrounds it: validation rules that check extracted values against your own records, confidence thresholds that decide what a person sees, and a review queue somebody owns. Measure field level accuracy and the share of documents that pass without a human touch, not a single headline accuracy number.Read Running AI in production on AWS and Azure: architecture, security, cost and operationsRunning AI in production on AWS or Azure means giving a model the same controls as any business system: identity, secrets, logging, cost limits, fallbacks and a named owner for every operating task. Managed services such as Amazon Bedrock and Azure OpenAI in Microsoft Foundry host the models, but the architecture, data boundaries and day-to-day operations remain your decisions to make and assign. This guide is for IT and operations leaders at companies of roughly 20 to 200 people.Read Building an AI assistant on your company knowledgeAn AI knowledge assistant answers questions from your own approved documents rather than general model training, using a pattern called retrieval-augmented generation. For a company of 20 to 200 people, buy Microsoft 365 Copilot, Gemini for Workspace or Glean when knowledge lives in one mainstream suite, and build when it is spread across line of business systems or answers must trigger work. The work is less about the model and more about choosing authoritative sources, enforcing existing permissions, showing citations people can check, and handling missing or conflicting information.Read Why AI pilots stall, and how to get one into productionAI pilots stall because of operational gaps visible from day one: no named owner, a demo on clean data and no plan for exceptions and approvals. This guide for companies of roughly 20 to 200 people covers seven decisions that let a pilot become production without a second project, plus the criteria for deciding whether to scale, fix or stop. It also shows what changes in monitoring, access, support and cost once a pilot goes live.Read AWS vs Azure for AI: Bedrock or Foundry, how to chooseFor most companies, the right cloud for AI is the one where their identity, data and technical team already sit. As of October 2026, Amazon Bedrock and Microsoft Foundry both offer models from OpenAI, Anthropic and other providers, so model choice rarely settles the question alone. Identity, data location, compliance scope, skills and existing agreements usually do, and this guide for companies of roughly 20 to 200 people puts the comparison table first.Read How to connect AI to your ERP, CRM and data warehouseConnect AI to your ERP, CRM and data warehouse through the vendor's supported APIs, webhooks, event streams or an integration platform, not by writing straight to the database. Start with read access, send every write through a named approver until the workflow has proven itself, and check API limits, licenses, permissions and data quality before you commit to a design.Read Planning a legacy application migration to the cloudA legacy application migration to the cloud starts with deciding, application by application, whether to retire, retain, rehost, replatform, refactor or replace it. Then map dependencies and data, set a cost baseline, plan cutover with a tested rollback, and treat security and identity as part of the move rather than an afterthought. Add AI or automation during the migration only where you are already changing that part of the system.Read Model-agnostic AI architecture: avoiding vendor lock-inA model-agnostic AI architecture puts one internal interface between your application and any model provider, so swapping models is a configuration change rather than a rewrite. It matters because providers publish retirement rules: Microsoft Foundry retires generally available models 12 to 18 months after launch, and OpenAI, Anthropic and Amazon Bedrock set notice periods from 45 days to 6 months. The real vendor lock-in usually sits elsewhere: in proprietary vector stores, agent frameworks, fine-tunes and contract commitments.Read Monitoring AI and LLM applications in production: what to trackMonitoring AI in production means tracking four things uptime checks miss: answer quality, cost per task, drift when inputs change, and whether the human approval step is keeping up. Log every model call and tool action with sensitive data protected, sample real outputs for review, and alert on thresholds a named owner agreed in advance. Write down how to roll back before you need to.Read RAG vs fine-tuning vs prompting: how to give AI your company's knowledgeUse RAG when the AI must answer from changing company documents and cite sources, and fine-tuning when it must behave consistently, such as a fixed format or narrow classification. Start with prompting, because a small, stable body of context often needs neither. This guide is written for business buyers at companies of roughly 20 to 200 people.Read SaaS subscription vs custom software: which costs less over five years?SaaS usually costs less over five years for standard processes with stable headcount, while custom software can win when per-seat fees grow and the process is specific to you. Model both with the same cost lines before deciding. This guide is for buyers at companies of roughly 20 to 200 people.Read Technical debt in business systems: how to spot it, prioritize it and pay it downTechnical debt is the extra effort every future change costs because of shortcuts, age and neglect in the systems you already run. Leaders see it as slow changes, fragile integrations, spreadsheets holding processes together, one person nobody can replace, and software versions the vendor no longer supports. Pay it down in slices where it blocks work you actually plan to do rather than through a full rewrite, and expect AI projects to expose it fast.Read How to test AI before deployment: test set, acceptance criteria and shadow modeTesting AI before deployment means scoring it against real past cases, with the business owner agreeing in advance what counts as correct and what target must be met. Run it in shadow mode or a limited pilot before it can change anything, keep human review on consequential decisions, and rerun the same test set every time a prompt or model changes.Read
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