2026-02-10 20:47:44
I usually ignore writing contests. Most of them feel either too marketing-heavy or too vague to be worth the effort. This one stood out because it’s clear, technical, and genuinely built around real developer work.
Iron Software is running a technical writing challenge focused entirely on IronPDF, and the idea is straightforward: write a real, useful article showing how IronPDF is used in practical development scenarios.
The top prize is $5,000, but honestly, that’s not the only reason this is worth doing.
You write one original article (minimum 1,000 words), and it must be about IronPDF. No generic PDF theory, no filler content.
You can approach it in a few solid ways:
This is probably the most natural option for most developers.
Examples:
Think: “Here’s a problem I had, and this is how I solved it using IronPDF.”
If you’ve worked with:
You can frame your article as a case study: what the problem was, what constraints existed, and how IronPDF fit into the solution.
If you’ve used alternatives like iText, Aspose, QuestPDF, or open-source tools, you can compare them — as long as the comparison is technical, honest, and experience-based.
Balanced comparisons actually read more authentic and human than forced praise.
This works well if you enjoy stepping back a bit.
Topics like:
IronPDF still needs to be central, but you’re free to discuss the bigger picture.
You don’t submit the article directly to Iron Software.
Instead, you publish it publicly on a developer platform like:
These platforms are marked as Bonus and typically carry higher visibility or authority:
These are widely used, developer-focused platforms and are perfectly valid for submission:
Don't forget to include a backlink to www.ironpdf.com
This is important because you keep the article. It lives on your profile, under your name, regardless of the contest outcome.
After publishing your article, there’s one simple extra step:
That’s it.
Even if you don’t win, your article stays public, indexed, and visible on platforms developers actually read — which is great for your portfolio, personal brand, and even job opportunities.
Judging is based on:
In short: Would another developer actually learn something from this?
Good technical writing compounds.
A solid IronPDF article can:
Worst case: you gain a strong, published technical article.
Best case: you gain that and $5,000.
If you already work with PDFs, reporting, or document automation, this challenge aligns naturally with work you’re probably doing anyway. No gimmicks, no vague prompts — just write something genuinely useful and submit the link.
That alone makes it worth considering.
2026-02-10 20:45:30
This is a submission for the GitHub Copilot CLI Challenge
I built a desktop app for Copilot to work on requirements in a Ralph loop mode. The app has a plan mode and also creates git commits for each task.
For those who prefer video you can watch the video demo, for readers here's what you can do in the app
@fileName
ashiqsultan
/
copilot-ralph
A desktop application to vibe code using GitHub Copilot. Define your tasks in a simple UI to build them in a Ralph loop way.
MacOS: Download link (universal dmg)
For checksum check Releases page.
You need to have copilot cli installed
If not please refer GitHub Copilot CLI Installation
The requirements are stored in plain JSON file Plan mode is optional, but recommended. The entire plan is sent as a single call to AI. The plan is stored for each requirement Starting the execution mode will pick the requirements one by one and build the application
Original post on Ralph loop. This is the post that triggered the Ralph way to use claude code. And I thought of building an UI for this for copilot because, TBH, Copilot is the chepest pricing I could find right now for AI assisted coding.
My app doesn’t strictly follow the OP, because I’ve moved some responsibilities out of the AI layer and into the orchestration layer. The system now handles the following tasks:
I made this way so the system is predictable and to reduce ambiguity of relying on AI
This is my first experice with AI in CLI (yes I dont have Claude code), before this I have used copilot only inside VSCode but using it in a CLI feels like more control. I liked how we can mention files with @ (which I also included in my app btw). I also loved the --yolo flag and I was mostly starting my sessions with yolo mode. Almost forgot to mention the --resume session was a saver and the information on how much premium request info at end of session was really helpful.
I must say this, choosing models in copilot felt more like a survival game. As I'm in a free tier I dont want to lose all my premium requests, since my app in itself is an UI for copilot, so testing the app also means additional calls so I followed this strategy. Sonnet 4.5 for normal tasks and Opus only when required so I dont use x3 the limit.
This is my typical flow
If you like the project a Github Star ⭐ would be great.
You can visit the webpage or Github to download and try the app.
2026-02-10 20:41:27
2026-02-10 20:40:48
LiteLLM has become a go-to starting point for teams building LLM-powered systems. At first, it feels like magic: a single library that connects multiple providers, handles routing, and abstracts away all the messy differences. For early experiments and small prototypes, it works so well that you barely notice what’s happening under the hood.
But as I started moving a LiteLLM-based system into production, the cracks began to show. Reliability, latency, memory usage, and long-running stability weren’t just minor annoyances anymore they were walls I kept running into.
I didn’t realize it at first, but LiteLLM alone wasn’t enough for the scale I was aiming for. That’s when I started looking into gateway-based architectures and the different ways teams solve these operational challenges.
LiteLLM solves a real and immediate problem: unifying access to multiple LLM providers behind a single interface. For teams experimenting with OpenAI, Anthropic, Azure, or others, it removes a lot of boilerplate.
It’s especially appealing because:
For small teams or early prototypes, LiteLLM often works well enough that there’s no reason to look elsewhere.
The issues tend to appear later.
As LiteLLM deployments grow in traffic and uptime expectations, several recurring problems begin to show up. These aren’t theoretical many are reflected in open GitHub issues.
At the time of writing, LiteLLM has 800+ open issues, which is not unusual for a popular open-source project, but it does signal sustained operational complexity.
A few representative examples:
Individually, each issue can often be worked around. Collectively, they point to a deeper pattern.
One recurring theme is that logging and persistence are tightly coupled to request handling. When a database sits directly in the request path, every call becomes vulnerable to:
As traffic increases, this can turn observability ironically into a performance liability.
Another common complaint is that services perform well initially, then slowly degrade:
For production systems expected to run continuously, this creates operational overhead and uncertainty.
At small scale, these issues are tolerable. At larger scale, they make capacity planning and SLOs difficult. Teams start compensating with:
At that point, the original simplicity starts to erode.
It’s tempting to assume these issues can be patched one by one. In practice, many of them stem from core architectural decisions.
LiteLLM is not primarily designed as a high-throughput, long-running gateway. It’s designed as a flexible abstraction layer. As usage grows, responsibilities accumulate:
Each additional responsibility increases pressure on the request path.
This is where the gateway model becomes relevant.
A gateway treats LLM access as infrastructure, not just a library. The core idea is separation of concerns:
This mirrors patterns already established in API gateways, service meshes, and reverse proxies.
Instead of embedding everything into the application runtime, the gateway becomes a dedicated control layer.
Bifrost takes this gateway-first approach seriously. Rather than positioning itself as a drop-in wrapper, it’s designed to sit between applications and LLM providers as a standalone system.
For more detailed documentation and the GitHub repository, check these links:
Several design choices are particularly relevant when contrasting it with LiteLLM.
One of the most important differences is that Bifrost does not place a database in the request path.
Logs, metrics, and traces are collected asynchronously. If logging backends slow down or fail, requests continue flowing.
The result:
This single decision eliminates an entire class of performance issues.
Bifrost is built to run continuously without requiring periodic restarts. Memory usage is designed to remain stable rather than growing unbounded with traffic.
This matters operationally:
For teams running gateways 24/7, this predictability often matters more than feature breadth.
Memory leaks and gradual accumulation are some of the hardest production problems to debug. Bifrost’s architecture prioritizes:
That reduces the need for manual intervention and defensive restarts.
The LLM gateway space offers several viable approaches, each optimized for different environments and team needs. Here’s a quick breakdown my top choices:
Strong focus on performance, stability, and gateway fundamentals. Designed for teams that want a dedicated, production-grade LLM control plane.
Well integrated into Cloudflare’s ecosystem. A solid option if you’re already using Cloudflare for edge networking and observability.
Optimized for Vercel-hosted applications. Convenient for frontend-heavy teams but more opinionated in deployment model.
Built on top of Kong’s API gateway. Powerful, but often heavier and more complex to operate.
Each option represents a different balance between control, simplicity, scalability, and ecosystem lock-in there’s no universal “best,” only what fits your stack and team maturity.
LiteLLM is often a good choice when:
Gateway-based solutions make more sense when:
Neither approach is universally “better.” They serve different stages of maturity.
LiteLLM plays an important role in the ecosystem, and its popularity reflects that. But as systems scale, architectural assumptions start to matter more than convenience.
Gateway-based solutions exist because teams consistently run into operational limits with long-running, high-throughput LLM workloads. Whether it’s Bifrost, Cloudflare AI Gateway, Vercel AI Gateway, or Kong AI Gateway, these platforms provide a predictable control layer, stable performance, and observability without slowing down requests.
If LiteLLM is starting to feel like a bottleneck rather than an enabler, that’s usually a signal not that you chose the wrong tool, but that your system has outgrown it.
At that point, evaluating gateway-based alternatives isn’t premature. It’s practical, and it helps you scale with confidence.
2026-02-10 20:35:42
Do you know about the wonderful ramen chain “Ramen Yamaokaya” in Japan?
Ramen Yamaokaya is a nationwide ramen chain founded in 1988 in Ushiku City, Ibaraki Prefecture, Japan. It's known for its rich tonkotsu broth and for allowing you to freely customize noodle firmness, flavor intensity, and fat content. Many locations are open 24 hours, making it beloved by truck drivers and night shift workers. I myself have been a fan for over 20 years. My home store is the legendary “Minami 2-jo Store” in Sapporo. I always order the shoyu ramen with less fat.
Last year's AWS Summit Japan 2025 inspired me to think about how I could support Yamaokaya within my area of expertise.
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So, I built an unofficial web application called “Yamaokaya Map.” This map lets you view store information for Ramen Yamaokaya locations nationwide.
This app supports PWA, so you can add it to your smartphone's home screen.
How to add:
Ramen Yamaokaya has four store types.
1. Ramen Yamaokaya
The standard Yamaokaya. Offers classic tonkotsu-based menu items. There are over 150 locations nationwide. I always order the Shoyu Ramen.
2. Niboshi Ramen Yamaokaya
A specialty shop serving niboshi (dried sardine) broth ramen. You can enjoy a different flavor profile from the standard Yamaokaya. I'm not a fan of niboshi, so I've actually never been.
3. Miso Ramen Yamaokaya
A shop specializing in miso ramen, known for its rich miso soup. Here, I recommend ordering the Shoyu Ramen deliberately. There are only 3 locations, all in Hokkaido.
4. Gyoza no Yamaokaya
A new concept store focusing on gyoza. There's only one location in all of Japan, located in Sapporo.
The map released this time uses icons to distinguish these four store types, and you can toggle their display on or off via layer switching.
Since I was going to perform scraping this time, I checked with the official website beforehand. They gave me a very warm response. Thanks to that, I immediately wanted to go eat there again.
This time, I'll use Python for scraping. I'll combine Playwright, pandas, and geopy to acquire and process the data.
yamaokaya-data
└── script
├── scrape_yamaokaya.py
├── latlon_yamaokaya.py
├── column_yamaokaya.py
├── csv2geojson.py
First, fork the Amazon Location Service v2 starter template. Then, add the files and code needed for the Yamaokaya Map.
MapLibre GL JS & Amazon Location Service Starter
Execution environment
yamaokaya-map
├── LICENSE
├── README.md
├── dist
│ └── index.html
├── img
│ ├── README01.gif
│ ├── README02.png
│ └── README03.png
├── index.html
├── package-lock.json
├── package.json
├── public
│ ├── manifest.json
│ ├── data
│ │ ├── yama.geojson
│ │ ├── niboshi.geojson
│ │ ├── miso.geojson
│ │ └── gyouza.geojson
│ └── icons
│ ├── yama.png
│ ├── niboshi.png
│ ├── miso.png
│ └── gyouza.png
├── src
│ ├── main.ts
│ ├── style.css
│ └── vite-env.d.ts
├── tsconfig.json
└── vite.config.ts
Install the package
npm install
Using the starter repository I forked, I’ll publish it on GitHub in the Amplify Console (Gen2), referencing an article I wrote previously.
https://memo.dayjournal.dev/memo/aws-amplify-016
The script scrapes store information from the official website. Since the official site dynamically generates content, I use Playwright to control the browser and retrieve the data. From each store's detail page, I extract the store name, address, phone number, business hours, parking information, seat types, shower room availability, the detail page URL, and the store's location information.
Example of retrieving the store name
from playwright.sync_api import sync_playwright
import pandas as pd
def scrape_yamaokaya_shops():
shops = []
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
context = browser.new_context(
user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
)
context.set_default_timeout(10000)
context.set_default_navigation_timeout(10000)
page = context.new_page()
main_url = "https://www.yamaokaya.com/shops/"
page.goto(main_url, wait_until='networkidle', timeout=10000)
page.wait_for_timeout(5000)
shop_links = page.eval_on_selector_all(
'a[href*="/shops/"]',
'els => [...new Set(els.map(el => el.href).filter(href => /shops\\/\\d+/.test(href)))]'
)
for url in shop_links:
try:
page.goto(url, wait_until='domcontentloaded', timeout=10000)
page.wait_for_timeout(5000)
name = page.evaluate("""() => {
const h = document.querySelector('h2, h1, .shop-name');
return h?.innerText?.trim() || document.title.split('|')[0].trim();
}""")
shops.append({'url': url, 'name': name or '不明'})
except Exception as e:
shops.append({'url': url, 'name': 'エラー'})
browser.close()
return pd.DataFrame(shops)
if __name__ == "__main__":
df = scrape_yamaokaya_shops()
df.to_csv('yamaokaya_shops.csv', index=False, encoding='utf-8-sig')
The location data scraped is in DMS (degrees, minutes, seconds) format. To display it with the map library, I convert it to DD format (decimal degrees). I use geopy to handle multiple conversion patterns.
Example of DMS→DD conversion
from typing import Tuple
from geopy import Point
# 変換前 "43°03'28.6""N 141°21'22.2""E"
def _convert_with_geopy(dms_string: str) -> Tuple[float, float]:
cleaned = dms_string.replace('""', '"')
point = Point(cleaned)
return point.latitude, point.longitude
Before converting the data to GeoJSON, I change Japanese column names to English.
Example of column name change
column_mapping = {
'店舗名': 'store_name',
'住所': 'address',
'電話番号': 'phone_number',
'営業時間': 'business_hours',
'駐車場': 'parking',
'座席の種類': 'seating_types',
'シャワー室': 'shower_room',
'その他': 'other_info'
}
df_renamed = df.rename(columns=column_mapping)
Finally, I convert the CSV to GeoJSON format. Files are output separately for each store type.
Example of CSV to GeoJSON conversion
import json
import pandas as pd
def create_geojson_features(df):
features = []
for _, row in df.iterrows():
properties = {}
for col in df.columns:
if col not in ['lat', 'lon']:
value = row[col]
if pd.isna(value):
properties[col] = None
else:
properties[col] = str(value)
feature = {
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [row['lon'], row['lat']]
},
"properties": properties
}
features.append(feature)
return features
GeoJSON output result
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [
141.3561,
43.0579
]},
"properties": {
"store_name": "ラーメン山岡家 南2条店",
"details": "https://www.yamaokaya.com/shops/1102/",
"address": "札幌市中央区南2条西１丁目6-1",
"phone_number": "(011) 242-4636",
"business_hours": "5:00－翌4:00",
"parking": "なし",
"seating_types": "カウンター席: 13",
"shower_room": "なし",
"other_info": "まちなかのちいさなお店です。"
}
},
For this project, I use MapLibre GL JS as the map library and Amazon Location Service for the background map.
import './style.css';
import 'maplibre-gl/dist/maplibre-gl.css';
import 'maplibre-gl-opacity/dist/maplibre-gl-opacity.css';
import maplibregl from 'maplibre-gl';
import OpacityControl from 'maplibre-gl-opacity';
const region = import.meta.env.VITE_REGION;
const mapApiKey = import.meta.env.VITE_MAP_API_KEY;
const mapName = import.meta.env.VITE_MAP_NAME;
const map = new maplibregl.Map({
container: 'map',
style: `https://maps.geo.${region}.amazonaws.com/maps/v0/maps/${mapName}/style-descriptor?key=${mapApiKey}`,
center: [138.0000, 38.5000],
zoom: baseZoom,
maxZoom: 20
});
I set up layers for each store type and assign custom icons to them.
interface LayerConfig {
name: string;
iconPath: string;
iconId: string;
visible: boolean;
}
const layerConfigs: Record<string, LayerConfig> = {
'gyouza': {
name: '餃子の山岡家',
iconPath: 'icons/gyouza.png',
iconId: 'gyouza-icon',
visible: true
},
'miso': {
name: '味噌ラーメン山岡家',
iconPath: 'icons/miso.png',
iconId: 'miso-icon',
visible: true
},
'niboshi': {
name: '煮干しラーメン山岡家',
iconPath: 'icons/niboshi.png',
iconId: 'niboshi-icon',
visible: true
},
'yama': {
name: 'ラーメン山岡家',
iconPath: 'icons/yama.png',
iconId: 'yama-icon',
visible: true
}
};
I add the GeoJSON data as layers. I configure the icon size to change based on the zoom level.
function addGeoJsonLayer(id: string, config: LayerConfig, data: GeoJSONData): void {
map.addSource(id, {
type: 'geojson',
data: data
});
map.addLayer({
id: id,
type: 'symbol',
source: id,
layout: {
'icon-image': config.iconId,
'icon-size': [
'interpolate',
['linear'],
['zoom'],
6, baseIconSize * 0.5,
10, baseIconSize * 0.6,
14, baseIconSize * 0.7,
18, baseIconSize * 0.8
],
'icon-allow-overlap': true,
'icon-ignore-placement': false,
},
paint: {
'icon-opacity': 1.0,
}
});
}
Clicking a store icon displays the store information in a popup. It shows the address, phone number, business hours, parking information, seating types, etc.
function createPopupContent(props: StoreProperties): string {
const contentParts: string[] = [];
if (props.store_name) {
contentParts.push(`<h3>${props.store_name}</h3>`);
}
const details: string[] = [];
if (props.address) {
details.push(`<strong>住所:</strong> ${props.address}`);
}
if (props.phone_number) {
details.push(`<strong>電話:</strong> <a href="tel:${props.phone_number}">${props.phone_number}</a>`);
}
// ...
}
Implemented layer toggling (show/hide) using maplibre-gl-opacity.
const overLayers = {
'yama': 'ラーメン山岡家',
'niboshi': '煮干しラーメン山岡家',
'miso': '味噌ラーメン山岡家',
'gyouza': '餃子の山岡家',
};
const opacityControl = new OpacityControl({
overLayers: overLayers,
opacityControl: false
});
map.addControl(opacityControl, 'bottom-left');
This time, I built the "Yamaokaya Map (Unofficial)" using a structure that includes Playwright for scraping, geopy for DMS→DD conversion and CSV→GeoJSON conversion, and map display via MapLibre GL JS and Amazon Location Service. Visualizing this on a map reveals new insights. The northernmost store is in Wakkanai. Stores are located in surrounding areas rather than central Tokyo. While they have expanded into Kyushu, there are no stores in Shikoku. And there is only one Gyoza no Yamaokaya store nationwide. This way, Ramen Yamaokaya's store opening strategy becomes clear.
Please use this when searching for a nearby store or looking for Ramen Yamaokaya while traveling!
2026-02-10 20:33:56
Browser split screen lets you view multiple web pages at the same time, so you can compare, reference, copy and paste without constant tab switching.
People use split screen in a browser for three main reasons:
Who benefits most:
In 2026, the best split screen tool is the one that matches your workflow style. There are two big approaches:
Official reference: Google Chrome Help: Use split view for multitasking
Chrome Split View is Chrome's built in split view feature. It lets you display 2 websites within a single Chrome window. One side is active, the other side is inactive, and most toolbar actions apply to the active side only.
Demo of Chrome Split View
Chrome Split View is the baseline in 2026, and you can treat it as the default for simple two pane tasks and as the fallback for strict sites. If your daily workflow needs more than two panes, repeatable layouts, or a true research workspace, you will outgrow it quickly.
Official: Dualless on Chrome Web Store
Stats: Last Updated 2023-12-29, Users 1M, Rating 3.95/5.0
Dualless is a classic split screen extension for people without a second monitor. It splits by creating separate browser windows with preset ratios.
Demo of Dualless
If your goal is two pages side by side, Chrome Split View is simpler and cleaner. Dualless only makes sense if you specifically want two separate windows and you accept the clutter tradeoff.
Official: Tab Resize on Chrome Web Store
Stats: Last Updated 2024-06-11, Users 1M, Rating 4.31/5.0
Tab Resize is a popular split screen tool that resizes the current tab and tabs to the right into layouts across separate windows. It is strong for multi monitor setups, shortcuts, and quick preset layouts.
Demo of Tab Resize
Tab Resize is best when you intentionally want window tiling, especially across multiple monitors. If you want a single tab workspace that stays organized, it is the wrong tool.
Official: PageVS on Chrome Web Store
Stats: Last Updated 2025-12-19, Users 669, Rating 4.67/5.0
PageVS is a new and modern split screen extension that turns one tab into a multi pane workspace. Instead of spawning many windows, it keeps everything inside a single tab and lets you freely arrange panes.
Demo of PageVS
PageVS is my personal favorite for doing research, comparisons, writing, and monitoring on a single screen. Use Chrome Split View only as a backup for the small number of sites that don't work well inside panes.
Official: Split Screen for Google Chrome on Chrome Web Store
Stats: Last Updated 2023-10-17, Users 300K, Rating 3.75/5.0
Split Screen for Google Chrome focuses on splitting and resizing browser windows into sections, often marketed for meetings and presentations. It is primarily a window management tool.
Demo of Split Screen for Google Chrome
For two pages, Chrome Split View usually replaces it. For serious research workflows, PageVS replaces it. This is a niche tool if you mainly want a simple window arran
Official: Split Screen on Mac on Chrome Web Store
Stats: Last Updated 2025-04-05, Users 6K, Rating 3.93/5.0
Split Screen on Mac provides preset layouts and utilities for moving and resizing windows. It aims to reduce manual window resizing effort.
Demo of Split Screen on Mac
If you prefer window based splitting and want preset snapping, it can help. For most users, Chrome Split View is enough for two panes, and PageVS is better when you need more than two panes.
Official: Tile Tabs WE on Chrome Web Store
Stats: Last Updated 2023-03-18, Users 80K, Rating 3.62/5.0
Tile Tabs WE is a powerful tiling tool that can arrange tabs into tiled sub windows, with many customization options and saved layouts.
Demo of Tile Tabs WE
Tile Tabs WE is for advanced tiling fans who can tolerate complexity and occasional rough edges. If you want a modern, reliable, single tab workspace with flexible layouts and less clutter, PageVS is the stronger default.
If you want the best split screen experience in 2026, pick based on your workflow:
For most people on a single monitor, PageVS gives the biggest productivity boost because it removes window clutter while adding real layout control.