Forget 'pass-by-reference'—Python variables are labels binding to objects via 'call by sharing'. Mutable defaults like [] create shared state across calls, causing ghost bugs; fix by using None and instantiating inside functions.
Build reliable AI code scanners by pitting a recall-focused hypothesis agent against a precision-focused evidence agent, stripping reasoning to avoid bias, and enforcing a deterministic policy gate—treating LLMs as stochastic machines, not oracles.
Agents fail without org-specific context; build a reasoning layer that personalizes retrieval, resolves conflicts, and respects permissions to deliver task-focused info, reducing task time from 2.5hrs/21M tokens to 25min/10M.
AI coding tools shrink writing time from ~4 hours/day to near zero, shifting effort to planning (saves 30min review per 5min upfront) and reviewing; parallelize agents past 5min executions to maximize throughput.
Legacy issue trackers like Jira provide durable state, ownership, handoffs, and audit trails—exactly what AI agents need for coordination, making them essential infrastructure despite human complaints.
For apps with 20-50 screens, use one root NavHost with nested feature graphs, centralized route objects, and layered state (nav args for IDs, ViewModels for data, composables for UI) to prevent navigation fragility.
After 50k AI page generations, boost streaming success from 92% to 99%+ by treating networks as foes: jittered backoff stops thundering herds, 90s health checks catch silent stalls, circuit breakers prevent self-DOS.
Apache Flink processes unbounded streams and bounded batches with one engine using operators, state, windows, and exactly-once guarantees, eliminating dual codebases for real-time apps like recommendation engines handling millions of events.
LLMs excel in verifiable domains like code via RL training, causing uneven abilities; embrace Software 3.0 by prompting agents end-to-end instead of coding rules.
After 20 years and 6,000 days of coding, ThePrimeagen feared AI devalued his skills—but realized experience prevents catastrophic choices like forking Chromium, making right decisions exponentially more valuable as code becomes cheap.
Andrej Karpathy describes evolving from 'vibe coding'—where anyone can build quickly with AI—to 'agentic engineering,' a disciplined practice coordinating jagged LLMs as 'ghosts' to ship production-quality software faster than ever.
Mario Zechner built Pi, a minimalist self-modifying AI coder powering OpenClaw. With Armin Ronacher, they praise its potential but warn against over-automation eroding code quality—human judgment remains key.
In GPT-5.5 tests on a Laravel/Filament task, xHigh used 44% session (4x Medium's 10%), took 14 min vs. 6 min, but added policies, extra tests, preloads—worth it for auth/data integrity risks.
Map LLMs to programming languages, MCP servers to libraries, skills to programs, context windows to RAM, and RAG to disk—use this analogy to compose and maintain agentic systems like traditional software.
Cross-App Access (XAA) uses IDJAG tokens from IDPs like Okta to exchange a single SSO login for short-lived access tokens across MCP servers, eliminating repeated OAuth consents and improving IT visibility/security.
Replace LLM text generation with a 5-layer pipeline that parses constraints, grounds in live data, validates outputs, scores quality, and regenerates low-confidence plans to deliver realistic itineraries.
BlockingLatch uses sync.Cond to enforce strict in-flight work limits, block producers immediately at capacity, reuse slots instantly on completion, adjust ceilings at runtime, and drain to zero on shutdown—ideal for streams under sustained pressure.
Thoughtworks' 34th Tech Radar (118 blips) spotlights AI trends like agent security and harness engineering, while urging return to basics like pair programming and clean code to counter AI-generated complexity.
Run a recommendation feed for 72,000 Bluesky users using one Go process on SQLite in a living room PC (16 cores/96GB RAM/4TB NVMe), proxying via $7 VPS over Tailscale, for $30/mo total—scalable to 1M DAUs.
Solo agents scale individuals but ignore team alignment, the real bottleneck. ACE provides multiplayer cloud sessions for collaborative planning, prompting, and coding to ensure teams build the right things.
AI agents introduce 'semi-executable artifacts' like prompts and workflows, expanding software engineering into a six-ring stack where outer rings—governance and societal fit—become critical engineering challenges, shifting focus from code to validation and maintenance.
Index repos into knowledge graphs with Tree-sitter ASTs to give Claude Code and Cursor full structural context via MCP tools, preventing dependency-blind changes in one query.
Mastering one framework teaches tools; a second exposes invariant problems that every stack solves, building transferable software skills.
Export Docker images as tar, parse manifest.json to decompress ordered layers revealing filesystem diffs from Dockerfile instructions, modify contents like os-release, then rebuild tiny custom images using FROM scratch—no base image needed.
AI amplifies bad code into expensive garbage; use deep modules, shared design concepts, and ubiquitous language to make codebases easy to change and AI-effective.
Design a relational restaurant database using supertype-subtype for transactions, enforce integrity with PK/FK/check constraints, query insights via JOINs/GROUP BY, simplify with views, and speed up with indexes—turns operations into queryable data for real efficiency.
Engineers at Meta, Microsoft, and Salesforce are 'token maxing'—running wasteful AI queries to hit leaderboards and avoid perf review scrutiny—echoing past lines-of-code pitfalls, yet AI drives individual productivity and broader role shifts.
Claude Opus 4.7 fixes persistence issues for better coding and agentic workflows but regresses in web research, uses 35% more tokens, and hallucinates task completion, costing more in real tests vs. GPT-4o.
AI agents enable rapid feature shipping, risking bloat and poor UX; Linear counters with deep customer insight, Zero Bug Policy, and Quality Wednesdays to build tasteful software that outlasts competitors.
Amid AI agents enabling instant shipping, Linear resists feature bloat via zero-bug policy, Quality Wednesdays, and ruthless prioritization—fixing 10% of bugs automatically while saying no to most requests.
Companies chase AI hype with flawed metrics like token leaderboards, calling agents 'employees,' and forcing use—real gains come from expert-AI synergy, not volume.
Defend AI orchestration layers by sanitizing prompt fillers against injections via pattern detection, classifying data to block PII leaks, tenant-scoping queries, minimizing context windows, and encrypting audit payloads—per OWASP's 21 GenAI risks.
Juniors outperform seniors on tickets shipped (14 vs 4) with clean PRs, but falter in incidents because they don't grasp the system's architecture—seniority means holding that mental model, not raw speed.
Use renderServerComponent in server functions to render React components on the server granularly, like fetching JSON. Composite components with slots keep client boundaries clean without 'use client' directives.
G2I launches Orchestrator AI, a multi-agent platform beating single agents on benchmarks like SWE-Bench by 8.4%; Dax argues AI's speed exposes our shortage of quality product ideas, urging restraint to avoid bloat.
Human programmers' laziness builds crisp abstractions to simplify code; AI bloats it. Use TDD for agent prompts (instructions first, then verification) and teach AI doubt to avoid overconfident errors.
Ditched Conductor and Cursor for Neovim-based workflow with Claude Code: replicates parallel agents, handles 7-8/10 code review complexity natively via LSP, no app-switching needed.
Don't delete unused take-home code—publish it publicly on GitHub, iterate with new patterns, and transform it into a showcase that attracts contracts elsewhere.
AI agents economically justify building software previously too costly, filling the full circle of needed automation and boosting demand for AI engineers. Start with 24/7 support, compressed research, info surfacing, and toil elimination agents today.
Replace JSON tool calling with AI-generated JavaScript code execution in sandboxes to handle massive APIs (e.g., Cloudflare's 2600 endpoints, 1.2M tokens reduced to 1K), enable stateful loops/parallelism, and unlock emergent behaviors like inspecting canvas strokes for tic-tac-toe.
Ditch JSON tool-calling for LLM-generated JavaScript code execution in capability-based sandboxes to handle 2600+ APIs in 1000 tokens (99.9% reduction), manage state/loops/parallelism, and enable generative UIs/workflows.
Coding agents like Claude Code make code generation cheap—hoard proven solutions, loop for better code, integrate Git/subagents, prioritize TDD/manual QA, and avoid unreviewed commits to ship higher-quality software faster.
Experienced Python developers waste hours assuming the language is 'fast enough,' leading to scripts ballooning from 2 seconds to 12 minutes on larger data—fix by vectorizing loops and caching computations.
ML system design interviews test narrowing the problem via clarification, capacity math with real numbers (QPS, storage, FLOPs), then architecture—skipping to diagrams fails.
Production agents fail on governance, context, persistence, and plumbing—bet on platform-level identity, universal context, durable execution, and standardized platforms to enable enterprise-grade autonomy.
Production agents fail due to stack limitations in identity, context, persistence, and platforms—bet on embedded governance, universal context, durable execution, and open primitives to enable enterprise-scale autonomy.
Production agents fail due to governance gaps from shared credentials, siloed context, fragile sessions, and custom plumbing—bet on platform-level identities, universal context, durable execution, and open platforms.
AI agents amplify code output 10x but breed addiction, massive PRs, brittle systems, and review overload. Fix with agent-legible modular codebases, linting enforcement, and deliberate human friction on critical changes like DB migrations.
Use Waterfall for stable requirements, regulated industries, fixed-price contracts where failure costs are catastrophic; Agile for uncertain needs, startups seeking product-market fit. Evaluate via 4 axes: requirements stability, change costs, team culture, project criticality.
Step-by-step code to create a self-contained background task system using Huey + SQLite: handle retries, priorities, pipelines, locking, scheduling, and monitoring—all runnable in a Colab notebook without Redis.
Rapid AI coding skips reviews, leading to surprise $800 Vercel bills from default high-cost settings; optimize builds (turbo to elastic saves 40x, sequential deploys) and learn fundamentals to avoid dependency risks.
Theo's hands-on tests reveal Claude Opus 4.7 shines in instruction-following and complex coding plans but regresses due to hyper-aggressive safeguards, buggy Claude Code harness, and outdated knowledge—making it dumber in practice than benchmarks suggest.
Anthropic's Claude Opus 4.7 shines in complex software engineering and instruction following but is undermined by excessive safety filters, buggy Claude Code harness, and outdated knowledge, leading to real-world frustrations.
Anthropic's Opus 4.7 shines in instruction-following, vision, and complex coding plans but fails on search, latest knowledge, and gets blocked by paranoid safety filters on benign tasks like puzzles or site design tweaks.
OpenAI engineer Ryan Lopopolo's team builds exclusively with AI agents by creating 'harnesses'—guardrails, skills, and prompts—that make codebases legible and execution reliable, freeing humans for systems thinking.
Code is free with capable LLMs like GPT-5.2; ban human editors, build harnesses with skills, prompts, lints, and reviewer agents to steer infinite agent capacity for full software engineering.
Opus 4.7 beats 4.6 by over 10 points on SWE-bench Pro, handles unsupervised engineering tasks better, uses file-based memory efficiently, and adds API task budgets—priced at $5/M input, $25/M output tokens.
Fully delegating dev workflow to AI sped up drafting but caused production issues like hollow tests, context-blind pipelines, AI self-reviews, and 34% webhook drop from unmodeled behavioral changes. Humans must supply context, break review loops, and validate impacts.
AI agents operate 50x faster than humans but gain only 2-3x productivity due to human-calibrated tools. Fix by rebuilding infrastructure in 3 layers; humans shift to 4 roles above the loop: generalist, pipeline builder, salesperson, overseer.
Human-speed web tools now bottleneck AI agents operating 10-50x faster and consuming 10-20k tokens/sec. Rebuild via optimized tools, agent-native primitives, and new scaffolding; humans shift to tool generalists, pipeline builders, business closers, and overseers.
Use precise prompts, plan mode for features, systematic debugging, and AI reviews in Cursor to turn coding agents into reliable software builders—start fresh convos, verify plans, reproduce bugs, self-review diffs.
Production LLM serving shifts from training's throughput focus to inference's memory-bound latency challenges, solved by PagedAttention (96% util), continuous batching, EAGLE-3 (up to 6.5x speedup), and FastLibra for multi-LoRA (63% TTFT cut).
Cursor, CodeX, Cloud Code, and upcoming VS Code agents mode share near-identical UIs for orchestrating agents across multiple projects, with integrated previews and feedback tools replacing traditional file trees and debuggers.
AI tools make generating code effortless, but precisely defining what code should do—specification—remains the hardest part, explaining why bugs and complexity persist.
Simple per-user rate limits on LLM APIs fail to stop credential stuffing attacks (causing $47K bills) and block critical clinical workflows; context-aware throttling with priority and anomaly detection is the only production-ready solution.
Vibe-code MVPs as a Pirate to find product value fast, then hand to an Architect to refactor into a reliable system—replacing traditional teams.
AI agent orchestration code (harness) drives 6x performance variation vs. model choice; natural language harnesses and automated optimization boost accuracy 16+ points while cutting compute 14x.
Centralize routes in sealed classes with helper functions, pass nav callbacks to screens, and use popUpTo(inclusive=true), launchSingleTop=true, restoreState=true for clean back stacks in auth flows, bottom tabs, nested graphs, and deep links.
Centralize routes in sealed classes, pass nav callbacks to screens, and use popUpTo/launchSingleTop for back stack control—patterns that prevent mess in real apps with auth, tabs, and flows.
AI hype caused tech chaos via fearmongering and over-reliance, but clarity returns by using AI as an accelerator for your original ideas—start tasks yourself, feed outputs to AI with detailed prompts, then refine to preserve uniqueness.
AI excels at any stack today, so developers must choose based on project performance needs, personal expertise, and code aesthetics—not AI biases or white coding.
Senior developers survive team reductions because they overlap responsibilities of PMs, BAs, UX, QA, DevOps, and PMs—AI cuts the cost of those overlaps, making them indispensable.
True programmer laziness drives abstractions for simplicity; LLMs lack this, generating massive unoptimized code like Garry Tan's 37k LOC/day 'newsletter' bloated with test harnesses, Hello World apps, and duplicate logos.
Rushing AI deployments trades speed for amplified future costs in data quality, model reliability, prompts, and governance; counter with strategic discipline and ready-aim-fire processes to build flexible, trustworthy systems.
Redux centralizes global state outside React's tree, uses selector subscriptions for re-rendering only changed slices, enforces unidirectional actions-to-reducers flow for auditability, and enables time-travel debugging via DevTools.
Production bugs hide like underground leaks—don't inspect directly; inject 'tracer gas' probes that force issues to surface, as a leak specialist did in 20 minutes without digging.
Software Engineering 3.0 shifts the unit of programming from syntax to intent—AI generates code from precise specs, while developers evaluate, orchestrate, test, and refine for correctness.
AI coding tools generate vulnerable code 40-72% of the time unless prompted for security; run this 30-minute 7-check checklist mapping to OWASP Top 10 to catch issues like exposed secrets and auth bypasses before deploy.
PostgREST turns any Postgres schema into a production REST API with CRUD, filtering, pagination, and RLS security—no controllers, routes, or ORM needed, cutting 80% of backend boilerplate.
Anthropic's unreleased Claude Mythos preview crushes coding benchmarks at 78% SWE-Bench and finds zero-day exploits in every major OS/browser, forcing a defensive alliance via Project Glasswing to patch vulns before public release.
Design MCP servers for agents using 5 principles to shrink attack surface and block OASP top 10 threats; deploy remotely via HTTP with OAuth 2.1, preferring CIMD over DCR for dynamic client auth.
Hackers used AI-crafted fake Slack workspaces and Teams calls to build trust over 2-3 weeks, tricking Axios maintainer into installing a RAT that published malicious npm packages 1.4.1 and 1.3.4 for 3 hours.
Connect Directus to Postgres/MySQL/Oracle for immediate REST/GraphQL APIs, field-level permissions, admin UI, file handling, and no-code flows—skipping all CRUD boilerplate and schema migrations.
Spot a PR flaw like missing docs? Define a correctness condition (e.g., all PRs update relevant docs) and add a reviewer agent to enforce it before tweaking coding agent instructions—guarantees compliance like test-first development.
Axios 1.14.1 & 0.30.4 compromised via fake crypto-js dep with post-install RAT stealing credentials; run OS-specific checks, rotate all secrets/API keys, use pnpm/bun min release age for prevention.
Treat SwiftUI views as functions of state (UI = f(state)). Choose wrappers by ownership: @State for local simple values, @Binding to share edits, @StateObject for view-owned models, @ObservedObject for injected ones. Compute derived state, persist with @AppStorage.
Replace fragile NavigationLink hacks with NavigationStack, typed Hashable routes, and a central router: enables programmatic pushes/pops, deep links, and isolated tabs without state bugs.
Set up a GitHub Actions workflow to auto-approve and merge Dependabot PRs for semver-patch and semver-minor updates after checks pass, reducing security patching overhead while enforcing CI/CD quality.
Use Apple's on-device Vision API for fast, private text recognition, rectangle detection, body pose estimation, and barcode scanning—clone the GitHub repo, follow the core request-handler pattern, and integrate with live camera feeds in SwiftUI for production-ready apps.
KernelBench's 250 NN tasks reveal LLMs generate compilable CUDA but falter on correctness for fused ops and architectures; agentic loops with profiling could enable near-peak GPU utilization.
Structure code with MVVM from day one, use SPM for dependencies, master SwiftUI state wrappers, centralize APIs, add tests and AppDelegate early, and leverage free Apple ID plus TestFlight to ship without setup headaches.
METR's KernelAgent, using o3-mini and others, achieves 1.81x average speedup on filtered KernelBench tasks via parallel tree search and high test-time compute, costing ~$20/task—far below human engineers for small ML projects.
Shift AI coding from fast generation to rapid verification using harnesses with sensors; structure functions to reveal intent; reject 'software brain' by prioritizing precise data definitions over total AI legibility.
2023-2025 tech layoffs (400k+) stemmed from over-hiring corrections targeting non-engineering roles; AI automates routine coding (25% at MS/Google) but drives demand for adaptive engineers, with 18% job growth projected to 2033.
Current AI optimizes for decisiveness, but true autonomy demands 'epistemic humility'—mechanisms to recognize knowledge limits and deliberately not act, inspired by Dark Star's bomb taught phenomenology for doubt.
Thoughtworks' 34th Radar shows AI dominating tech trends, forcing revisits to core practices like pair programming and clean code to counter generated complexity, while emphasizing security for permission-hungry agents and human review of AI code.
Core roles include PO for vision, PM for execution, BA for insights, designers for UX, engineers for code, QA for quality. Size teams 4-8+ based on discovery/prototype/MVP phases, complexity, budget, deadlines to hit market fast without waste.
Build engineering strategy through explore-diagnose-refine cycles, using systems models and Wardley Maps for validation, as shown in Uber migrations, Stripe API deprecations, and LLM adoptions.
Insomnia v12 GA adds MCP client support, AI-powered commits, natural language mock servers, and free tier with unlimited projects and Git sync for 3 users.
Use a 'full layer cake' MVVM with Models, Repositories, Services, ViewModels, and Views to separate concerns in SwiftUI apps, enabling testability, maintainability, and growth without monolithic views.
Codex Security, an AI agent, analyzes repos for vulnerabilities, builds threat models, tests exploits, reduced false positives >50% and redundant alerts 84%, flagged 792 critical vulns in 1.2M commits.
Encourage LLMs as tools that amplify human responsibility, rigor, empathy, teamwork, and urgency—use for reading, editing, debugging; avoid for writing prose; reject mandates or shaming.
16 Opus 4.6 agents in parallel autonomously produced a 100k-line Rust C compiler that builds Linux 6.9 on x86/ARM/RISC-V after 2,000 sessions and $20k API cost, revealing harness designs for long-running LLM teams.
Separate planning and execution roles let hundreds of agents collaborate on massive projects, generating 1M+ lines of code over weeks while minimizing conflicts and drift.
Thoughtworks' Structured Prompt-Driven Development (SPDD) treats prompts as versioned artifacts via REASONS Canvas and CLI workflow, scaling AI assistants from solo speedups to team-safe, reusable code generation.
Structured Prompt-Driven Development (SPDD) treats prompts as versioned artifacts using a REASONS canvas and workflow to make AI-generated code governable, reviewable, and reusable across teams.
Use iOS 16+ NavigationStack with Hashable Route enums as path data for clean, scalable navigation—programmatic pushes, deep links, and tabs without state hacks or spaghetti code.
Extreme Programming counters software entropy in evolutionary design with testing, continuous integration, refactoring, and simple design rules like YAGNI, balancing minimal upfront planning with ongoing evolution over rigid Big Design Up Front.
Don't build features needed 6 months out now—incur build costs, 2 months revenue delay, ongoing carry costs, and 2/3 chance they're useless or wrong anyway.
