Developer Builds Custom AI Agent Using DeepSeek After Finding Claude Too Costly

·1 views

A developer from a Latin American background turned to DeepSeek's AI models after finding Anthropic's Claude financially out of reach. DeepSeek, which released its models in May, offered a low-cost alternative with no country restrictions, IP bans, or credit card requirements. The developer was struck by the speed and quality of responses, describing the experience as a turning point in how programmers can interact with AI tools. Inspired by the accessibility of DeepSeek, the developer decided to build a personal AI coding agent using the platform. The piece reflects a broader sentiment among developers in cost-sensitive regions who seek powerful AI tools without prohibitive pricing.

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)

Classic BLoC vs BlocSignal: How Flutter handles rapid .add() event calls

Flutter developers often worry that calling .add() multiple times in quick succession may cause events to overwrite each other before the UI can respond. In Classic BLoC, events are queued through Dart Streams, with each handler executing sequentially and the UI updating only on the next Flutter render frame. BlocSignal follows the same .add() and emit() API but replaces Streams with Signals, making handler execution synchronous so bloc.state reflects the new state immediately after .add() returns. In both implementations, no events are lost even when thousands are dispatched rapidly, because Flutter's markNeedsBuild() simply flags a widget as dirty once and rebuilds it once per frame using the latest state. The key distinction between the two approaches is propagation timing, not event reliability.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Local-First Interview Prep Tool Using Go and Static HTML

A developer has created a self-hosted interview preparation system called a 'Local-First Interview Prep Gym,' built with Go's standard library and static HTML, requiring no accounts or external APIs. The tool addresses gaps in typical prep workflows by providing structured daily drills, pattern-recognition exercises, and code-reading training. Users can run different tracks from the command line, covering data structures, backend interview topics, and reading comprehension of code solutions. A rotating set of weekday specialty drills covers ten algorithm pattern families, while five core functions are practiced every session with a target completion time of under eight minutes. The project emphasizes consistency over intensity, with a minimum daily session of 20 minutes and a forward-only approach that eliminates catch-up pressure for missed days.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Step-by-Step Guide to Deploying the ELK Stack on Kubernetes with ECK

The Elastic Stack — comprising Elasticsearch, Logstash, Kibana, and Beats — enables real-time log collection, processing, storage, and visualization. A technical guide published on DEV Community walks through deploying the full stack on Kubernetes using the Elastic Cloud on Kubernetes (ECK) operator. The setup requires a Kubernetes cluster with at least three nodes and 4GB RAM each, along with kubectl and Helm configured and a domain for Kibana access. ECK automates Elasticsearch cluster configuration, scaling, and upgrades, while Logstash handles pipeline processing and Filebeat ships logs from each node. The guide covers operator installation, Elasticsearch cluster definition, credential retrieval, and Logstash pipeline configuration with Filebeat integration.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Engineering team cuts 1.2B-row query time from 2,100ms to 48ms using partitioning and caching

The engineering team behind Mattrx, a multi-tenant marketing-analytics SaaS, reduced dashboard KPI query latency from 2,100ms to 48ms on a 1.2-billion-row Azure SQL table. The original system ran raw aggregations across the entire table on every dashboard load, pushing database CPU to 78% under a peak load of around 3,200 requests per second from 110,000 monthly active users. The solution involved three key architectural changes: partitioning the CampaignEvents table by day, applying clustered columnstore indexing for compression and batch-mode aggregation, and pre-computing daily KPI rollups so dashboards query summaries instead of raw events. A Redis cache layer with a 30-second TTL was added on top to serve the hottest dashboard requests in roughly 2ms. After the changes, peak database CPU dropped to 22% and working memory shrank from 2.1GB to 380MB.

0 comments Read more at DEV Community