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ProgrammingDEV Community ·

Computer Science Graduate Builds Career in SEO and Content Strategy

A computer science graduate describes how a technical degree led them into SEO and content strategy rather than software engineering. In 2015 they co-founded TechLookBD, which they ran alongside their work for about five years. Their undergraduate project, Autometify, was a touch-based home automation controller. They also held a senior executive role at AjkerDeal.com and an SEO analyst position at Web Integrator Ltd, a US-based agency that went remote in 2019. They now lead SEO strategy at TechLookBD and mentor SEO learners at Prayogik.com, arguing that the degree gave them a grounding in how websites work.

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ProgrammingDEV Community ·

AI 에이전트 개발 문서를 관리하는 방법 가이드

이 가이드는 AI 에이전트를 위한 문서를 코드와 함께 레포 안에서 버전 관리하고, 유효 기간과 적용 범위에 따라 계층으로 나누라고 권한다. 에이전트는 세션마다 기억이 초기화되므로 레포에 명시된 내용만 사실로 다루도록 설계해야 한다. 상시 규칙은 CLAUDE.md에 짧게 적고 상세 내용은 docs 폴더에 두어 필요할 때만 읽게 한다. 요구사항이 바뀌면 코드보다 스펙 문서를 먼저 수정하고, 기술 결정은 ADR로 기록해 추가만 하는 방식을 따른다. 반복되는 실수는 문서 한 줄보다 테스트, 린트 규칙, Hook 같은 기계적 장치로 막는 것이 권장된다.

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ProgrammingDEV Community ·

A Simple Habit for Tracking Money Lent to Friends Without Awkward Follow-Ups

Informal loans between friends are often never recorded, and the resulting mismatched memories can strain relationships more than the money itself. The article argues that writing down each loan immediately, including the person, amount, date, purpose, and expected repayment date, acts as a fair safeguard for both sides. It also recommends logging debts you owe to keep the arrangement balanced. Because individual loans tend to form patterns that memory cannot reliably track, the author suggests choosing a method, such as a notes app or spreadsheet, that fits your everyday habits. Taking about fifteen seconds at the time of the transaction is presented as far easier than awkwardly chasing repayment months later.

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ProgrammingDEV Community ·

Structured Meeting Notes and Same-Day Recaps Drive Follow-Through

Many teams take meeting notes but few actually use them, and the gap comes down to how the notes are organized. The recommended format lists decisions in one sentence each, actions with an owner and deadline, and open questions with an assigned owner, while skipping a transcript of who said what. Recaps sent the same day are far more likely to be read than those sent days later, so they should also be kept short enough to read on a phone. Starting each meeting by reviewing the previous meeting's action items reinforces accountability. The article also notes that AI note-takers can generate recaps automatically, provided a person verifies decisions and owners before distribution.

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ProgrammingDEV Community ·

Developer builds Windows gauge estimating Claude Code usage limits from local logs

A developer who uses Claude Code heavily created a small Windows gauge that shows an estimate of remaining usage in the corner of the screen. The tool reads the append-only session logs Claude Code already writes, counting input, output and cache-creation tokens while excluding cache reads, which would distort the estimate. Because plan limits are unpublished and change over time, it learns a limit from past 'limit reached' messages or from the user's busiest recent window, and projects when the user may run out. The same approach is said to work with Codex, Gemini CLI and Antigravity logs, while Cursor only offers an activity estimate. The tool is an unofficial, independent product not affiliated with Anthropic, is sold after a 7-day free trial, and its author has asked for feedback on the estimation method.

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ProgrammingDEV Community ·

Ollama silently drops tool calls to functions missing from the tools list

When a model served by Ollama calls a function absent from the request's tools list, the call is discarded and the reply comes back empty, with no content and no tool_calls. The response still reports generated tokens through eval_count, showing the model produced output that was thrown away. In tests on Ollama 0.40.8 with qwen3:0.6b, eight of eight replies were empty when the needed tool was left out, while adding it to the list produced the expected call every time. The behavior stems from Ollama's generic, template-based tool parser, which affects models without a dedicated parser, including qwen3. The suggested workaround is for clients to list every tool the model might call, and the issue is tracked upstream as ollama/ollama#19187.

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ProgrammingDEV Community ·

Docker graphdriver store mislabels zstd image layers as gzip on push

Docker daemons using the graphdriver image store, including Docker 28 and upgraded Docker 29 installs, relabel zstd-compressed layers as gzip when pushing an image they pulled. The layer data itself is reused unchanged, so the push succeeds and docker run still works, since Docker detects compression from the bytes rather than the label. Tools that trust the label, such as BuildKit building FROM the relabelled image with forced zstd compression, fail with a missing compression blob error. Without forced compression the build succeeds but carries the incorrect gzip labels into the new image. The problem was reproduced on Docker 29.9.1 and 29.9.2, while the containerd image store labels the same layers correctly, and it is tracked upstream in moby/moby#53999 and moby/buildkit#7308.

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ProgrammingDEV Community ·

Mediation Analysis Explained: How Indirect Effects Reveal Research Mechanisms

Researchers often want to know not just whether two variables are related, but how one influences the other. Mediation analysis tests whether an intermediate variable, or mediator, helps explain that link, using an example in which social media marketing may affect purchase intention through brand awareness. The approach relies on two paths: the effect of the independent variable on the mediator, and the effect of the mediator on the outcome, whose product gives the indirect effect. Because the indirect effect's sampling distribution is often non-normal, researchers typically use bootstrap confidence intervals to assess its uncertainty. The guide also cautions that finding an indirect effect does not by itself establish causation, which depends on research design and assumptions.

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ProgrammingHacker News ·

OpenAI Releases Official Command-Line Interface on GitHub

OpenAI has published a command-line interface tool in a public GitHub repository, according to a Hacker News post. The post gained 15 points and drew six comments from the community. The source material provides only the repository link and engagement metrics, so details about the tool's features, capabilities, and release date are not confirmed here. Readers interested in its functionality should consult the GitHub repository directly.

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ProgrammingDEV Community ·

Guide Shows How to Pull SEC EDGAR Filings as JSON Without a Custom Scraper

A developer tutorial explains how to retrieve SEC EDGAR filing data in structured JSON form for alerts, compliance checks, and equity research. It notes that EDGAR's endpoints are anonymous and require no API key, but that requests must include a declared User-Agent and stay under 10 requests per second. The article presents steadfetch/sec-edgar-monitor, an Apify Actor that wraps these endpoints and accepts filters by company CIK, form type, date range, or full-text query. Sample code in curl, Node.js, and Python is provided, along with an example filing record showing fields such as accession number, form type, and filing date. Pricing for the Actor is listed at 5.00 USD per 1,000 filings.

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ProgrammingDEV Community ·

Developer Builds AI Chat Assistant Using Agentic LLMs and Multi-Tool Integration

A developer shared how they built an AI chat assistant that goes beyond a standard chatbot by connecting a large language model to external tools. The project was motivated by the limits of standalone LLMs, which may lack current information and cannot independently access external services. The article explains agentic AI, where the model interprets a request, selects and executes a tool, and then folds the returned results into its reply. Using a weather query as an example, it describes how the assistant would call a weather service rather than rely on the model's training data. The author also links the live demo and the frontend and backend source repositories for readers to explore.

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ProgrammingDEV Community ·

Kruskal–Wallis Test: A Nonparametric Method for Comparing Three or More Groups

The Kruskal–Wallis test is a nonparametric statistical technique for comparing three or more independent groups. It serves as an alternative when data fails to meet the assumptions required for a one-way ANOVA, and it is applicable in fields such as education, business, psychology, and healthcare. The test checks whether group distributions differ, and it requires independent groups, ranked outcomes, and independence of observations. Its output is an H statistic and a p-value; a p-value below a threshold like 0.05 suggests at least one group differs, but does not identify which ones, so follow-up tests may be necessary.

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ProgrammingDEV Community ·

Web HIG advises 24px minimum tap targets and 44px for primary touch actions

The Web HIG guidance states that interactive targets must be at least 24 by 24 CSS pixels, matching WCAG 2.2 Level AA, while primary touch actions should ideally measure 44 by 44 pixels. The clickable area, not the visible icon, is what counts, so small icons can be made easier to hit by adding padding. Tightly packed controls such as close buttons, table row actions, and pagination links are common problem areas, since a miss can trigger the wrong action. The guidance notes that people with tremors or limited dexterity, not just touch users, benefit from larger targets. Developers are advised to measure the clickable element's box in browser developer tools and test primary actions by hand on a phone.

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ProgrammingDEV Community ·

Push-sync design for Google Sheets and n8n avoids full re-reads on every run

Cron workflows that read an entire Google Sheet each run incur heavy costs, since the Sheets node downloads everything before narrowing results, which becomes slow at around 50,000 rows. The article notes the Visualization query endpoint can read a single column cheaply but is only suited to queries, not synchronization. The recommended alternative inverts the flow: the sheet pushes row edits to n8n via an installable Apps Script trigger, and n8n maintains a projection of the data. Row numbers are used only for diagnostics because they shift when rows are sorted or deleted, while identity is keyed on an ID column. Invalid payloads are logged and rejected with a 422 response, since the script caller fails silently by default.

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ProgrammingDEV Community ·

How to Find the Project Behind a Port on macOS Using lsof

A common problem for developers is opening localhost:3000 and finding the wrong app, since every dev server appears as node in process lists. The article explains that macOS lacks Linux's pwdx command, but lsof can reveal the working directory of the process listening on a given port. It walks through using lsof with the -sTCP:LISTEN flag to isolate the server, then the cwd descriptor to print the folder it was launched from. The guide also covers retrieving the full command line with ps, the start time, and the git branch, and offers a reusable shell function called whoport to combine these steps. It further notes that the same approach helps distinguish multiple worktrees or servers running in the same repository.

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ProgrammingDEV Community ·

Guide to two-stage retrieval and reranking for balancing recall, precision, and latency

The guide explains how to separate candidate retrieval from relevance reranking in search-and-generation systems. It describes a funnel in which a fast first stage pulls a broad set of candidate passages and a slower reranker narrows them to the most relevant few. A table maps common symptoms, such as low answer relevance, high latency, token budget overruns, and missed clauses, to likely root causes and checks. The article also outlines component responsibilities, latency targets, and deterministic filtering rules, along with a sample function for tuning top-N and top-k within token and latency limits. It closes with monitoring practices such as tracking hallucination rates and keeping configuration documentation in sync.

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ProgrammingDEV Community ·

How to Structure Product Demos That Convert, Plus a Review Checklist

This guide explains how product demos, which are short focused presentations of a product solving a real problem for a specific buyer, can be made more persuasive. It recommends showing the outcome first, then only the few steps needed to reach it, so viewers do not lose track. The article outlines the main demo formats, including live sales demos, recorded walkthroughs, interactive tours, and short social clips, each suited to different stages of the buying journey. It also explains that demos help buyers see the product working for someone like them, which both builds interest and filters out poor-fit prospects early. Finally, it offers practical advice on recording and polishing demos, such as using realistic sample data and keeping the screen readable.

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ProgrammingDEV Community ·

Fixed vs random effects in Python: what a Hausman test rejection really means

A DEV Community tutorial walks through choosing between fixed and random effects panel models using the wage_panel dataset from linearmodels, covering 545 individuals over eight years. The author computes a Hausman statistic by hand, getting chi-squared of 112.118 with 3 degrees of freedom and p near zero, which rejects the random effects assumption. The piece cautions that such a rejection only suggests RE is inappropriate and does not prove fixed effects is correct, since time-varying confounders remain. It also notes that fixed effects cannot estimate coefficients on time-invariant variables and that the classic Hausman test assumes homoskedastic errors, so the Mundlak regression-based approach is suggested for clustered standard errors. The author also mentions an online panel regression tool they are affiliated with.

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ProgrammingDEV Community ·

Databricks Interview Questions Lean Toward Classic System Design, Not AI

An analysis of 29 reported Databricks system design questions finds that 24 are classic system design problems, while only 5 concern AI systems or ML. The most frequently reported questions are coding problems, several of which appear at no other company tracked in the dataset. The coding screen is a 60-minute live session in a runnable editor, drawing repeatedly from a small set of four problems. Candidates identify concurrency as the hardest part of the interview loop, with seven questions testing it. The comparison suggests that candidates preparing for AI labs like OpenAI or Anthropic will still need to study classic system design for Databricks.

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ProgrammingDEV Community ·

LinguaPal: Multi-Agent AI App Offers Personalized English Tutoring

LinguaPal is a web application that uses Google Gemini and five specialized AI agents to help learners practice English. It covers reading, pronunciation, listening, writing, and spelling through lessons matched to CEFR proficiency levels. The app adjusts difficulty over time, analyzes voice input, and gives personalized feedback. It also uses a spaced repetition system built on SQLite, based on the SuperMemo SM-2 algorithm, to schedule review. The project was submitted under the AI and Machine Learning category, with its source code and a demo video publicly available.

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