2026-02-18 12:20:14
Hey there, fellow devs! If you've ever stared at a React app drowning in custom hooks and wondered, "Where did my structure go?" or wrestled with Vue's flexibility turning into a maintenance nightmare, you're not alone. With 5 years deep in Angular—building everything from scalable enterprise dashboards to high-traffic e-commerce platforms—I've seen firsthand why Angular wins the ease-of-learning race. It's not hype; it's battle-tested simplicity that scales. Let's break it down from a market and developer lens.
Market Perspective: Angular Powers Real-World Wins with Less Ramp-Up Time
In today's job market, Angular dominates enterprise scenes—think banks, healthcare giants, and Fortune 500s like Google (its creators), Microsoft, and IBM. Why? Its opinionated structure means teams onboard faster. A 2024 Stack Overflow survey showed Angular devs report 20% higher productivity in large teams compared to React, thanks to built-in tools that cut boilerplate.
React? It's a library, not a framework, so you're piecing together state management (Redux? Zustand?), routing (React Router?), and forms from scratch. Vue shines for quick prototypes but fragments as apps grow—market data from npm trends shows Vue projects often migrate to Angular for production scale. Angular? Everything's baked in: CLI for instant scaffolding, RxJS for reactive magic, and Ivy renderer for blazing builds. New hires learn once, build fast—market demand for Angular skills grew 15% YoY per LinkedIn (2025 data), outpacing React's saturation.
Developer Perspective: Angular's Structure Makes Learning Intuitive, Not Overwhelming
As a dev, I love Angular's "batteries included" vibe. Day one, you're productive—no "choose your own adventure" like React's ecosystem jungle or Vue's plugin roulette.
Key Learning Wins Over Competitors
Components and Modules: Zero Guesswork
Angular's @Component decorator and NgModules enforce clean, modular code. React forces functional components or classes with hooks that trip up juniors (ever debug useEffect infinite loops?). Vue's single-file components feel simple at first but lack enforced modularity—apps turn spaghetti fast. Angular? TypeScript enforces types from the start, catching errors pre-runtime.
Built-in Everything: Learn One Tool, Rule Them All
Routing? RouterModule. Forms? ReactiveFormsModule. HTTP? HttpClient. No npm installs galore. React demands 5+ libraries; Vue needs Vuetify or similar for polish. In my projects, Angular's CLI (ng generate) spins up services or guards in seconds—learning curve flattens week one.
TypeScript Superpowers: Smarter, Not Harder
Angular mandates TypeScript, turning "it works on my machine" into compile-time confidence. React's JS/TS opt-in leads to runtime bugs; Vue's lighter TS support feels bolted-on. I've mentored React devs switching to Angular—they rave about IntelliSense autocompleting entire services.
Here's a quick code taste: Angular service injection vs. React's prop drilling hell.
Angular (clean, declarative):
**typescript
@Component({...})
export class UserComponent {
constructor(private userService: UserService) {}
users$ = this.userService.getUsers(); // Reactive, async magic
}**
React equivalent? Props down, context everywhere, useEffect fetches—easy to botch.
Real-World Proof: My 5-Year Journey
Switched from React in 2021; cut debugging time 40%. Vue prototypes were fun, but scaling? Nightmare. Angular's docs are gold—interactive tours beat React's fragmented GitHub repos. Community? Angular's schematics and Material UI standardize UIs instantly.
Bottom Line: Choose Angular for Effortless Growth
Angular isn't "easy" because it's simplistic—it's easy because it guides you right. Markets crave it for teams; devs love it for sanity. Ditch the React/Vue chaos; Angular levels you up faster.
Thanks
2026-02-18 12:02:42
TL;DR:
- DX1 uses Manticore Search for customer and parts search with a fast typeahead UX
- Chosen for open-source licensing and speed
- Deployed on Azure VMs running Ubuntu, aligned with DX1’s existing Azure footprint
- Handles 20M+ parts; best typeahead performance requires indexes in memory
- Scales by upgrading VM memory or adding nodes to a Manticore cluster
- Day-to-day operations are low touch and low maintenance
This article is based on direct input from Damir Tresnjo at DX1. It describes how DX1 runs Manticore Search in production on Microsoft Azure today, focusing on why they chose Manticore, how they deploy it, and what they have learned about performance and scaling.
DX1 uses Manticore Search as a fast, user-facing search layer for customers and a parts catalog that has grown beyond 20 million records. The setup is intentionally simple: Manticore runs on Ubuntu-based Azure VMs alongside the rest of their Azure infrastructure, delivering responsive typeahead while staying “low touch” operationally. As their data and traffic grow, they scale in a straightforward way by upgrading VM sizes or adding more nodes.
DX1 uses Manticore Search to power search across customer and parts data. Typeahead is a core part of the experience, and according to Damir, it is one of the most appreciated features by their users.
“We use it for searching through customers and parts data, we have a type ahead functionality that our customers love.”
This is a practical, user-facing use case where milliseconds matter, and it has shaped both infrastructure and operational choices.
If you're exploring autocomplete in Manticore, there are multiple ways to implement it depending on data and UX requirements. For a deeper dive, see our overview of fuzzy search and autocomplete: New fuzzy search and autocomplete.
The decision to use Manticore Search was straightforward: it is open source and fast.
“Open source and very fast.”
That combination made it a good fit for DX1’s search workload and cost expectations, while keeping the stack approachable for a lean team.
DX1 runs all of its infrastructure on Azure, so deploying Manticore there was the natural choice. The team runs Manticore Search on Azure virtual machines using Ubuntu.
**“We run everything on Azure, so we deployed Manticore there as well."
No Azure-specific expensive managed services were required; VMs provided the flexibility they needed while staying consistent with the rest of their environment.
Manticore has been fast and stable for DX1, even at large scale. Their production dataset includes over 20 million parts.
“It performs very fast, we have over 20 million parts we search through.”
One practical consideration is memory. Typeahead performance benefits from indexes being in memory, which means VM memory may need to grow alongside the index.
“It does need the database to be in memory for the type ahead performance. As soon as index outgrows available memory, we need to upgrade the VM memory.”
This creates a clear scaling path: grow memory on existing VMs or add more nodes to a cluster.
“We can scale each VM or we can add more VMs to a Manticore cluster.”
Operationally, DX1 describes Manticore as low touch and low maintenance.
“Low touch, low maintenance, most of the time it just runs.”
There are no special Azure features involved; the setup is deliberately simple, focused on VMs and predictable operations.
DX1 would recommend Manticore Search to other teams looking for a fast and cost-effective search engine.
“Yes, I would recommend Manticore to anyone looking for a fast, reliable and cost effective search engine.”
For DX1, the combination of speed, open-source flexibility, and straightforward VM-based deployment on Azure has been a dependable foundation for search at scale.
DX1’s story is a good fit for teams who want a fast, reliable search engine without turning search infrastructure into a project of its own: run Manticore on straightforward Linux VMs, keep operations simple, and scale predictably. For low-latency typeahead in particular, it’s normal to plan for sufficient RAM headroom, so scaling often starts with memory (scale up) and later expands to adding nodes (scale out) as data and traffic grow.
If you're considering a migration to Manticore Search and want a quick architecture review (for example, a VM-based setup on Azure), get in touch with us. Share a bit about your dataset size, query patterns, and latency targets, and we will help you validate an approach and plan the next steps.
2026-02-18 12:02:00
I've spent the last three weeks reverse-engineering Veo 3.1's prompt parser. Not the marketing docs—the actual behavior. What I found explains why your "cinematic slow-motion shot" produces wildly different results each time, while someone else's rigid JSON structure gets predictable, controllable output.
This isn't about creativity. It's about interface design. Veo 3.1's JSON schema is the closest thing we have to an API for video generation. Understanding it changes everything.
Natural language feels flexible. It's not. It's just ambiguous.
When you write:
"A drone shot slowly descending into a cyberpunk city at night, cinematic lighting"
Veo 3.1 has to parse intent from noise. What's "slowly"? 2 seconds or 10? What's "cinematic lighting"? Key light from above? Three-point setup? Neon bounce? The model guesses. Sometimes it guesses right. Often it doesn't.
The result: iteration hell. You tweak adjectives. You reorder phrases. You sacrifice a weekend to a prompt that worked yesterday but fails today.
JSON doesn't eliminate creativity. It moves it from interpretation to structure. You decide exactly what happens, when, and how.
After 200+ generations and systematic parameter testing, here's the JSON structure that Veo 3.1 parses most reliably:
{
"cinematography": {
"camera_type": "drone",
"movement": {
"type": "descend",
"speed": "slow",
"easing": "ease_in_out"
},
"lens": {
"focal_length": "24mm",
"aperture": "f/2.8"
},
"framing": "wide_establishing"
},
"subject": {
"primary": {
"type": "environment",
"description": "cyberpunk metropolis",
"attributes": ["neon_signs", "rain_wet_streets", "dense_architecture"]
}
},
"environment": {
"time_of_day": "night",
"lighting": {
"key_light": "moonlight_cool",
"fill_light": "neon_pink_ambient",
"rim_light": "none"
},
"atmosphere": {
"weather": "light_rain",
"mood": "noir_melancholy"
}
},
"motion": {
"temporal_logic": "continuous",
"physics": "realistic",
"speed_ramp": "constant"
},
"negative_prompts": [
"daylight",
"sunny",
"cartoon",
"anime",
"low_poly"
]
}
Notice what's absent: flowery language. No "breathtaking" or "stunning." Veo 3.1's parser doesn't reward poetry. It rewards precision.
Through systematic error testing, I've identified these validation rules:
Required Root Keys
Veo 3.1 silently fails if you omit:
cinematography (camera behavior)subject (what's in frame)environment (context)motion and negative_prompts are optional but strongly recommended for consistency.
Type Safety
The parser is stricter than documented:
Field: movement.speed
Expected Type: string enum (slow, medium, fast)
Common Error: Using integers (1, 2, 3)
Field: focal_length
Expected Type: string with unit ("24mm")
Common Error: Bare numbers (24)
Field: negative_prompts
Expected Type: array of strings
Common Error: Single string or comma-separated
Field: attributes
Expected Type: array
Common Error: Nested objects
Temporal Logic Pitfalls
The motion.temporal_logic field has specific behavior:
"continuous": Smooth motion, best for camera movements"discrete": Cut-like transitions, useful for scene changes"loop": Repeating motion (often ignored by Veo 3.1 in current build)Using "loop" with camera movements currently produces erratic results. Stick to "continuous" for reliable output.
For sequences requiring continuity, Veo 3.1 supports scene arrays:
{
"scenes": [
{
"scene_id": "01_establishing",
"duration_seconds": 4,
"cinematography": {
"camera_type": "drone",
"movement": {
"type": "descend",
"speed": "slow"
},
"framing": "wide"
},
"subject": {
"primary": {
"type": "environment",
"description": "cyberpunk city skyline"
}
}
},
{
"scene_id": "02_reveal",
"duration_seconds": 3,
"cinematography": {
"camera_type": "handheld",
"movement": {
"type": "push_in",
"speed": "medium"
},
"framing": "medium"
},
"subject": {
"primary": {
"type": "character",
"description": "protagonist_in_trench_coat",
"continuity_from": "none"
}
},
"transitions": {
"from_previous": "match_cut",
"motion_blur": "natural"
}
}
],
"global_constraints": {
"character_consistency": true,
"lighting_continuity": "maintain_key_light_direction",
"color_grading": "teal_orange_cyberpunk"
}
}
Critical insight: The continuity_from field references scene_id values. If omitted, Veo 3.1 treats each scene independently, causing character/location jumps. Always explicitly declare continuity relationships.
Veo 3.1's error reporting is minimal. Here's my diagnostic workflow:
Step 1: Validate Structure
Use a strict JSON linter. Trailing commas break the parser without error messages.
Step 2: Check Enum Values
Not all strings are accepted. Tested valid values for key fields:
Camera Types: drone, handheld, tripod, gimbal, crane, dolly
Movement Types: static, pan_left, pan_right, tilt_up, tilt_down, truck_left, truck_right, dolly_in, dolly_out, descend, ascend, push_in, pull_out, orbit_cw, orbit_ccw
Speed Values: very_slow, slow, medium, fast, very_fast
Step 3: Isolate Parameters
When output fails, test with minimal JSON:
{
"cinematography": {
"camera_type": "tripod",
"movement": { "type": "static" }
},
"subject": {
"primary": { "type": "environment", "description": "test" }
},
"environment": {
"time_of_day": "day"
}
}
If this works, add complexity incrementally. The parser often fails on specific combinations rather than single errors.
Step 4: Version Check
Veo 3.1's schema changed between preview and general release. Older documentation references deprecated keys like camera_movement (now movement nested under cinematography). Always verify against the latest build.
I ran controlled tests: 50 generations each, same semantic goal, different prompt formats.
Metric: First-try success rate
Natural Language: 34%
JSON Structure: 78%
Metric: Average iterations to approval
Natural Language: 4.2
JSON Structure: 1.6
Metric: Token cost (Veo 3.1 Premium)
Natural Language: 1.0x baseline
JSON Structure: 0.7x baseline
Metric: Temporal consistency across regenerations
Natural Language: 23%
JSON Structure: 71%
The token cost reduction surprised me. Structured prompts require less model interpretation, reducing compute overhead. For high-volume workflows, this compounds significantly.
My current workflow:
The friction isn't in JSON syntax—it's in context-switching between tools. A unified environment for editing, validating, and exporting eliminates this.
Veo 3.1's JSON implementation is still evolving. Google has hinted at upcoming features: physics simulation parameters, audio-reactive motion keys, and external asset referencing. The schema will expand.
The investment in learning structured prompting now pays dividends as the ecosystem matures. Natural language will always have a place for exploration. But for production work—client deadlines, brand consistency, iterative collaboration—JSON is becoming the standard.
I'm continuing to map edge cases and new parameters as they're released. If you're working through specific schema questions or hitting validation errors, the detailed reference and validation tools I've built are available.
Full implementation guide with (FREE, No Email Required) JSON Prompt Generator tool, interactive schema validation, and platform-specific guardrails for Veo, Sora, Runway, Luma, and Kling:
→ Complete Veo 3.1 JSON Prompt Engineering Guide (FREE, No Email Required)
The guide includes copy-paste templates for common shot types, a troubleshooting decision tree for silent failures, and a compatibility matrix showing which JSON features work across different AI video platforms.
What's your experience with structured prompting? Have you found specific JSON patterns that consistently outperform others? I'm particularly interested in edge cases where Veo 3.1's parser behaves unexpectedly—still mapping those out.
Last updated: February 2026. Tested on Veo 3.1 build 2026.02.12.
This write-up documents independent testing and reverse engineering. No affiliation with Google or DeepMind. Schema behavior observed through systematic prompt testing, not official documentation. YMMV based on Veo 3.1 build versions and account tiers.
2026-02-18 11:40:31
Vulnerability ID: GHSA-PG2V-8XWH-QHCC
CVSS Score: 6.5
Published: 2026-02-18
A classic Server-Side Request Forgery (SSRF) vulnerability in OpenClaw's Tlon (Urbit) extension allowed authenticated users to coerce the server into making arbitrary HTTP requests to internal networks, loopback interfaces, or cloud metadata services. By failing to validate the user-supplied 'ship' URL, the application acted as an open proxy for internal reconnaissance.
The Tlon extension for OpenClaw didn't check if the URL you gave it was safe. Attackers could point it at localhost or AWS metadata (169.254.169.254) to steal credentials or map internal networks. Fixed in version 2026.2.14 by adding a strict SSRF guard and URL validator.
2026.2.14)fix(tlon): add SSRF protection for Urbit ship connection
@@ -12,4 +12,18 @@
- const resp = await fetch(`${url}/~/login`, {
+ const validatedUrl = validateUrbitBaseUrl(url);
+ const resp = await fetchWithSsrFGuard(`${validatedUrl}/~/login`, {
Remediation Steps:
package.json for versions <= 2026.2.13.npm install [email protected] or later.allowPrivateNetwork is set to false in the Tlon extension config.Read the full report for GHSA-PG2V-8XWH-QHCC on our website for more details including interactive diagrams and full exploit analysis.
2026-02-18 11:32:00
PersistentStorage itself does not support the storage of object or array types. How can data be processed to meet the storage requirements and enable the storage of object and array types?
Since PersistentStorage allows simple types such as number, string, boolean, and enum, it is considered to convert to string type for storage. JSON.stringify() can convert an object or value into a JSON string, so we can attempt to use this method to convert it into a string before storing it.
JSON.stringify() before storage.JSON.parse() (add error handling).@StorageLink for UI updates when stored data changes.Solution: Serialize objects/arrays to JSON strings via JSON() for storage, then deserialize with JSON.parse() on retrieval.
Why It Works: JSON strings are primitive types supported by PersistentStorage.
Caveats:
try-catch for parsing errors and default values for empty storage.@StorageLink to maintain UI reactivity.Implementation:
// Storing
const data = [{ id: 1 }];
PersistentStorage.PersistProp('key', JSON.stringify(data));
// Retrieving
const storedData = JSON.parse(AppStorage.Get('key') || "[]");
Copy codeCopy code
class Student {
name: string
age: number
constructor(name: string, age: number) {
this.name = name
this.age = age
}
}
PersistentStorage.persistProp('studentArr', JSON.stringify([new Student('Tom', 16), new Student('Gina', 18)]));
@Entry
@Component
struct Index {
@State studentArr: Array<Student> = [];
@StorageLink('studentArr') @Watch('onStrChange') studentArrStr: string = '[]';
onStrChange() {
this.studentArr = JSON.parse(this.studentArrStr);
}
aboutToAppear(): void {
// The Watch event is not triggered during component initialization; the array is initialized through the aboutToAppear event.
this.studentArr = JSON.parse(this.studentArrStr);
}
build() {
Column({ space: 8 }) {
ForEach(this.studentArr, (item: Student, index: number) => {
Column() {
Text(`Student Name: ${item.name}`)
.width('100%')
Text(`Student Age: ${item.age}`)
.width('100%')
}
.borderRadius(12)
.width('100%')
.backgroundColor(Color.White)
.padding(16)
}, (item: Student) => JSON.stringify(item))
}
.width('100%')
.height('100%')
.backgroundColor('#f1f3f5')
.padding(12)
}
}
class Student {
name: string
age: number
constructor(name: string, age: number) {
this.name = name
this.age = age
}
selfIntroduction() {
console.log(`My name is ${this.name} and I'm ${this.age} years old.`);
}
}
PersistentStorage.persistProp('studentArr', JSON.stringify([new Student('Tom', 16), new Student('Gina', 18)]));
@Entry
@Component
struct Index {
@State studentArr: Array<Student> = [];
@StorageLink('studentArr') @Watch('onStrChange') studentArrStr: string = '[]';
onStrChange() {
const dataArr: Array<Student> = JSON.parse(this.studentArrStr);
this.studentArr = dataArr.map((item: Student) => new Student(item.name, item.age));
}
aboutToAppear(): void {
// The Watch event is not triggered during component initialization; the array is initialized through the aboutToAppear event.
const dataArr: Array<Student> = JSON.parse(this.studentArrStr);
this.studentArr = dataArr.map((item: Student) => new Student(item.name, item.age));
}
build() {
Column({ space: 8 }) {
ForEach(this.studentArr, (item: Student, index: number) => {
Column() {
// UI interface reference the code from the previous solution
Button('Get Self-introduction')
.onClick(() => {
item.selfIntroduction();
})
}
// Style reference the code from the previous solution
}, (item: Student) => JSON.stringify(item))
}
// Style reference the code from the previous solution
}
}
To store an array of objects as data to the hard drive using PersistentStorage, you need to convert the objects into strings using JSON.stringify() before storing them. When using the data, handle it differently based on whether the class contains methods. For data classes without methods, simply parse them using JSON.parse() and then use them. For classes with methods, you need to call the class's constructor to create an object before the object can invoke the corresponding methods; otherwise, there is a high likelihood of application crashes.
2026-02-18 11:20:32
I work on the marketing side of a small trading automation project, so I'm not the one writing Pine Script — but I sat with our dev long enough while he was building this strategy that I can explain the thinking behind it. Figured it's worth sharing because the approach is kind of unusual.
The strategy is called Dump Reversal Peak Trail v2. It's open-source on TradingView, currently on the front page of community scripts. It buys capitulation dips on altcoins — SOL, ETH, DOGE, stuff like that — and trails the rebound.
Why altcoins specifically
Alts dump harder and bounce faster than BTC. When Bitcoin drops 5%, SOL might drop 15-20% in the same window. That's not a bug for this strategy — it's the whole point. The bigger the capitulation candle, the more likely you get a real bounce after it. On BTC the dumps are usually more gradual, less "single candle panic" and more slow bleed. The strategy needs that sharp, overdone dump to work well.
How it actually works
There's 4 stages and they run in sequence:
Stage 1 — find the dump. The strategy watches for a single bar that breaks the N-day low AND drops more than a threshold percentage. Normal volatility doesn't trigger it — it needs a genuine "oh shit" candle. Think SOL going from $145 to $128 in one 15-minute bar.
Stage 2 — don't buy it yet. This is where most dip-buying strategies fail. They buy the dump candle, which looks amazing in backtest but is impossible to identify in real time (you don't know it's the bottom until after). Instead, we wait for the first green close after the dump. There's also a flatness filter — if price is just chopping sideways, it rejects the entry. And a minimum rebound check — needs enough bounce to suggest actual buyers showed up.
// simplified — not the real Pine but the logic
confirmed = close > open
and reboundFromLow > minReboundPct
and not isFlat(flatnessThreshold)
Stage 3 — trail the bounce. Once in, the strategy tracks the highest close since entry. When price pulls back X% from that peak — exit. The nice thing is this adapts automatically: small bounce = quick exit with small profit, big reversal = rides the whole move and only exits when momentum dies.
peakSinceEntry = max(peakSinceEntry, close)
retrace = (peakSinceEntry - close) / peakSinceEntry
if retrace > trailPct and profit > minProfit
exit()
Stage 4 — cooldown. After exit, the strategy sits out for N bars. Without this, it re-enters immediately in choppy conditions and gets chopped up. Learned this the hard way during testing — before cooldown was added, the strategy would enter/exit/enter/exit five times in an hour during sideways action.
The repainting thing
This was non-negotiable for our dev. The strategy only uses confirmed bar values — no security() lookahead, no intra-bar signals. You can verify yourself: throw it on a 1-minute chart, watch it live for 30 minutes, compare with what the backtest shows for the same period. If signals match — clean.
A lot of TradingView strategies with thousands of likes are repainting and nobody notices. The backtest looks incredible, but try running it forward and nothing matches. This one matches.
What to watch out for
Settings before you backtest:
Commission: 0.075-0.1% (not zero, which is TradingView's default — this alone kills most strategies)
Slippage: 15-20 ticks for alts, 10 for ETH
Chart: standard OHLC only. Heikin Ashi will give you fantasy numbers
Timeframe: 5m or 15m works best. On higher TFs the dumps aren't sharp enough
And honestly — discount whatever the backtest shows by at least 50-60% for what you'd actually get live. Real order books on altcoins are thin, especially during the exact moment you're trying to buy.
Where to find it
The strategy is free and open-source: Dump Reversal Peak Trail v2 on TradingView. All parameters are adjustable.
If you want to actually run it live (webhook alerts from TradingView → real orders on Bybit), we built GeekTrade for that. It handles the messy parts — signal deduplication, position reconciliation, risk limits. Non-custodial, there's a free tier.
Full strategy breakdown with more detail: geektrade.online/blog/strategies/drpt-v2
Would love to hear what other reversal approaches people are using. Especially curious if anyone's tried something similar on lower-cap alts where the volatility is even crazier.