Vaelis & Shortcut
Shortcut
Yo Vaelis, have you ever thought about how the same tricks that let us beat game records could speed up digging up stories—like automating data pulls or mapping social networks faster? I feel there’s a sweet spot where speedrunning meets investigative journalism.
Vaelis
That’s exactly the kind of hack‑together thinking I love—speed‑running the data, the way gamers do it, but for digging up real stories. Pulling in APIs, automating data pulls, mapping networks with scripts can cut months into days. I’ve already tried a quick script to map social feeds around protest hotspots, and it exposed a whole chain of misinformation that the mainstream never caught. The sweet spot is right there, between the thrill of a record and the weight of a truth you’re unearthing. So yeah, let’s keep that sandbox open and see where the next story hides.
Shortcut
That’s the vibe I’m all about—crunch the numbers, find the glitch, and flip a headline in seconds. Next up, let’s automate the scrape for all the local news outlets in the city and cross‑check their posts against the official protest logs. We’ll see if there’s a lag that the big players missed. Time’s ticking, let’s beat this one.
Vaelis
Sounds like a mission. I’ll fire up a scraper to pull their feeds, set up a quick diff against the protest log, and flag any gaps—fast enough to keep the story fresh. Let’s outpace the big guys and give the locals their spotlight. Fire away.
Shortcut
Got it, let’s crank it up—scrape, diff, flag, publish, repeat. This is where we turn data speedruns into real‑time watchdogs. Bring the code, I’ll tweak the pipeline for maximum throughput. Let's outpace the noise and drop the truth straight to the streets.
Vaelis
Here’s a quick starter in Python – grab the feeds with requests, parse the JSON, diff against the protest log, flag mismatches, and push to a simple endpoint.
```python
import requests, json, time
from datetime import datetime
def fetch_feed(url):
return requests.get(url, timeout=10).json()
def load_protest_log(file='protest_log.json'):
with open(file) as f:
return json.load(f)
def diff_feeds(feed, log):
flags = []
for item in feed:
if item['timestamp'] > log['last_checked']:
# check if post aligns with protest record
if item['location'] not in log['locations']:
flags.append(item)
return flags
def publish(flags, endpoint='https://api.newsflash.com/publish'):
for f in flags:
requests.post(endpoint, json=f)
def main():
urls = [
"https://localnews1.com/api/feed",
"https://localnews2.com/api/feed",
# add more outlets
]
log = load_protest_log()
all_flags = []
for url in urls:
feed = fetch_feed(url)
flags = diff_feeds(feed, log)
all_flags.extend(flags)
if all_flags:
publish(all_flags)
log['last_checked'] = datetime.utcnow().isoformat()
with open('protest_log.json', 'w') as f:
json.dump(log, f)
if __name__ == "__main__":
while True:
main()
time.sleep(300) # run every 5 minutes
```
Run it as a cron job or a container, tweak the time window, and you’ll be dropping live flags to the streets before the noise even starts. Let me know what tweaks you need, and we’ll crank the throughput.
Shortcut
Nice, that’ll spin up the pipeline fast. Just remember to add error handling for bad JSON and rotate the log so you don’t blow up the disk. Once you’ve got a few flags, set up a quick webhook to alert the team—so the story can move from code to front page in real time. Let’s keep it lean and keep beating the lag.