SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Developer builds Telegram job alert bot using 60 lines of Python and live ATS data

0
·1 views

A developer has shared a minimal Telegram bot built in approximately 60 lines of Python that delivers real-time job alerts by querying the HiringIndex API, which pulls data directly from applicant tracking systems. The bot filters postings by job title, remote status, and recency, then sends each new listing to a Telegram chat exactly once using a simple text file to track seen postings. No database or web framework is required, making it easy to deploy via a cron job for daily morning alerts. The HiringIndex API also offers a separate insights endpoint that provides salary and market data per currency, distinct from the search results. A free tier offering 200 postings and five insights calls per month is available without requiring a credit card.

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)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Memory API Vendor Builds Open Benchmark for Its Own Industry, Publishes Conflict-of-Interest Rules

A developer at Wontopos, a company that sells memory APIs, has created an open benchmark called Glasshouse to evaluate memory systems, acknowledging the inherent conflict of interest in administering a benchmark they also compete in. To address this, the company has published written rules stating that Wontopos submissions undergo the same third-party approval process as all others, and the firm imposes a 14-day embargo on publishing its own results after any new version. The benchmark dataset is built around a single simulated person's 17-month conversation history, spanning over 103,000 turns and roughly 1.9 million tokens, designed to test memory recall across varying context sizes. A key design choice is a three-tier scoring system for outdated facts, distinguishing between a correct updated answer, an honest admission of uncertainty, and a confidently wrong stale answer — treating the last as a distinct failure mode. The full codebase is released under the Apache 2.0 license, allowing any party to fork and independently operate the benchmark if Wontopos itself becomes a source of bias.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

AI search engines recommend crypto solutions just 6 times out of 360 queries

A study by one-person studio Kunzum tested whether AI engines like ChatGPT, Perplexity, and Gemini recommend crypto products when users describe real financial problems such as costly remittances or frozen bank accounts. Across 360 unprompted responses collected from five AI engines, crypto was cited as a usable option only six times, while Wise alone was recommended 246 times. The researcher attributes crypto's near-invisibility to technical issues including JavaScript-heavy frontends that AI crawlers cannot parse, poorly maintained documentation, and marketing activity concentrated on platforms like Twitter Spaces and Telegram that leave no retrievable documents. Unlike fintech rivals that publish structured comparison pages optimised for AI retrieval, most crypto projects rely on content formats that language models cannot index or cite months later. The findings suggest that in AI-driven search, crypto products suffer not from lack of quality but from a near-total absence of the crawlable, factual content that determines which solutions get recommended.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Why AI-Generated UI All Looks the Same and What Designers Can Do About It

AI design tools consistently produce interfaces dominated by purple-to-blue gradients, Inter font, rounded cards, and generic hero sections — a pattern researchers call distributional convergence. Rather than reasoning about brand identity or user needs, AI models statistically gravitate toward the most common patterns in their training data, producing competent but indistinct results. Tailwind CSS creator Adam Wathan acknowledged the trend in August 2025, half-jokingly apologising for seeding the indigo color default that influenced countless AI-generated interfaces. A 2024 Nature study formally documented the mechanism, showing that models trained on generated data lose variance over time and converge toward a narrower, more homogeneous output. The problem worsens as AI-generated designs re-enter training pools, making the resulting aesthetic increasingly polished yet harder to distinguish from genuinely considered design work.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Brazilian Student Pivots from Traffic Management to Tech After 30+ Courses

Maycon, a Systems Analysis and Development student at UNICID in Brazil, is transitioning into software development after a career in customer service, sales, and paid traffic management. Over the past few months, he has completed more than 30 courses and hands-on projects through platforms like DIO and Santander Open Academy. His studies have focused on Git, GitHub, Python, and AI-assisted automation tools including Claude Code, GPT Agents, and GitHub Copilot. Among his practical projects is a Porsche-themed business intelligence dashboard featuring KPIs and interactive charts built with AI assistance. He shared his story on DEV Community to connect with fellow developers and seek his first professional opportunity in tech.

0 comments Read more at DEV Community