Setting Up Infolanka News Updates for Reliable Monitoring
I have been running Infolanka News Updates on a few different setups over the past couple of years, mostly for tracking local Sri Lankan current affairs across multiple regional outlets. The tool itself is straightforward, but there are a few things that trip people up if you try to use it blindly. Here is how I approached it.
Infolanka News Updates: What It Actually Does
At its core, Infolanka News Updates aggregates news feeds from a set of Sri Lankan media sources and pushes them through a configurable pipeline. You give it source URLs or RSS endpoints, you set your refresh intervals, and it either sends you notifications or dumps the content into a file or database. That is the basic version. The more useful angle is using it as a watchtower. I ran it to monitor about fourteen sources simultaneously, tracking keyword shifts, headline frequency, and sometimes cross-referencing stories between English-language and Sinhala-language outlets to catch translation delays.
Installation and Basic Configuration
The setup depends on whether you are using the standalone desktop version or the server variant. I have used both, and I will walk through the server path since it is the one that actually scales past a personal hobby project. Step one: grab the latest release from the official GitHub repository. Do not use third-party forks unless you are comfortable auditing their code, because I have seen cases where a fork silently dropped HTTPS verification to speed up fetches. That sounds helpful until your provider starts getting IP blocks for suspicious traffic patterns. Step two: configure your sources. The default configuration file is JSON, and the key fields are sources, intervals, and filters. The sources array takes objects with url, name, and language properties. Add your targets here. A practical list includes the main news portals, the BBC Sinhala feed, and at least one independent blog that tends to break stories early.
Get the Full Details
Step three: set the refresh interval. The default is five minutes, which is fine for breaking news but inefficient if you are just looking for daily summaries. I switched mine to thirty minutes and added a keyword filter so the system would only alert on specific terms. This cut my notification volume from roughly two hundred items per day down to about forty. Step four: choose your output format. Infolanka News Updates supports plain text, JSON, and CSV. JSON is the most flexible if you plan to pipe the output into another tool. CSV is easier if you want to open it in a spreadsheet and do quick manual review.
Real-World Configuration Problems and Workarounds
One thing nobody mentions in the documentation is how aggressive some Sri Lankan news sites are with anti-bot measures. I ran into this repeatedly with the Daily Mirror feed. After about three days of polling every five minutes, the site started returning CAPTCHA challenges instead of the RSS content. My pipeline would then error out silently and I would miss whatever story was running that day. The fix was simple enough once I found it. I added a rotating user-agent header in the configuration and set a random delay between requests, somewhere between fifteen and forty-five seconds. This mimics normal browsing behavior closely enough to bypass the basic detection. The tradeoff is that your monitoring becomes slightly slower, but you stop getting blocked entirely. Another edge case: several outlets publish content in Sinhala script without declaring it in their RSS metadata. The default parser reads the encoding as UTF-8 anyway, but if your terminal or output file does not support Sinhala characters, the feed will show as garbled text. I solved this by explicitly adding "encoding": "utf-8" to each source entry and then piping the output through a small Python script that validates character completeness before writing to disk. This adds about forty seconds of processing overhead per fetch cycle, but it eliminates the garbled output problem completely.
Advanced Use: Cross-Referencing Story Origins
Once the basic pipeline is stable, the most valuable feature is the built-in similarity matching. Infolanka News Updates includes a deduplication module that compares headline phrasing and body text across sources. When the same story appears on multiple sites, it links them together and timestamps each publication event. This is where the tool gets interesting. In the Sri Lankan media landscape, certain stories consistently originate from a handful of outlets before being picked up by the rest. By tracking the timestamps across sources, I was able to identify which outlets were breaking original reporting versus those that were reposting. I documented this for about six months, and the pattern was remarkably consistent. A few specific investigative outlets regularly broke stories first, and the major papers would reproduce them within two to four hours, usually without attribution. You can export this data and run your own analysis, or you can use the built-in visualization dashboard if you are running the Pro version. The dashboard shows a simple timeline graph of each story's origin and spread across sources.
Limitations and Where It Falls Apart
I should be honest about what this tool does not handle well. It cannot reliably scrape content behind paywalls. If a source requires a subscription, Infolanka News Updates will fetch the URL but you will get a login page or a truncated excerpt. There is no workaround for this within the tool itself. It also struggles with JavaScript-heavy single-page applications. Some newer news sites have moved away from RSS entirely and serve content through dynamic rendering. The standard fetcher in Infolanka News Updates uses a basic HTTP client, which means it will not execute JavaScript. For these sites, you need to either find an undocumented RSS endpoint or pair the tool with a headless browser wrapper like Puppeteer. That adds complexity and reduces the reliability of your pipeline. Finally, the deduplication algorithm is not perfect. It uses a combination of headline similarity and body text hashing, which works well for straight news reports but performs poorly on opinion pieces and analyses that share a common topic but use different framing. You will get false positives when two unrelated stories happen to cover the same event from opposite angles.
If you need more robust deduplication, a better approach is to export the raw feed data and run it through a proper NLP pipeline using something like spaCy or a fine-tuned transformer model. This is more work but gives you significantly better accuracy on story grouping.
Quick Reference Summary
The basic setup takes about twenty minutes on a fresh server. Configuration file editing accounts for most of that time, followed by testing each source individually to catch encoding issues and CAPTCHA blocks before you commit to a full run. I recommend starting with five to seven sources maximum. Once you confirm the pipeline is stable, gradually add more. Adding everything at once makes it much harder to isolate which source is causing errors. The tool runs comfortably on a low-end VPS. I have had it running on a two-core, four-gigabyte instance without any issues, handling about fifteen sources at a thirty-minute interval. If you push it beyond twenty sources with five-minute intervals, you will start seeing memory pressure and occasionally dropped fetches during peak network latency periods.
For most people, thirty-minute intervals with ten to twelve sources is the sweet spot. You lose the ability to catch truly breaking stories within minutes, but you gain stability, lower resource usage, and a manageable notification volume.
