2026-02-25 09:41:04

If you like VS Code REST Client, you're going to love LAZYREQUEST

You already know the best format for API testing: a plain .http file.

What you probably don't have is a dead-simple way to run those files from the terminal (and CI), validate the responses you expect, and get a clean PASS/FAIL signal without adopting an entire new testing framework.

Meet LAZYREQUEST: a lightweight, minimal API Testing Client CLI tool inspired by VS Code REST Client syntax. It recursively discovers .http and .rest files, sends the requests, and compares actual responses against the expected responses you declare in the same template.

Site | npm | GitHub

If you've ever thought:

• "My API smoke tests should be as easy as writing an HTTP request."
• "I want contract-ish checks without standing up a whole test harness."
• "Why does 'try this endpoint' live in Slack instead of in the repo?"

This is for YOU!.

TL;DR

• Write requests the same way you already do in VS Code REST Client: method, URL, headers, body.
• Optionally declare an expected response block right in the file (status + headers + body).

• Run lazyrequest on a folder. It finds everything, executes fast (concurrent by default), and tells you what broke.

• No new syntax.

• No heavy GUI.

• No learning curve.

• No Postman tab explosion.

Just .http files. And power.

No collections. No click-ops. No "where did we store the latest curl command?"

The Problem Nobody Talks About

Let's say you are working on a API and you need to test it, you can quickly write a .http file with something like:

GET http://localhost:8080/health

You can see it's output. It works. It’s clean. It’s readable.

But then:

• You need CI integration

• You want automated assertions

• You want folder-wide execution

• You want concurrent runs

• You don’t want a browser UI

Suddenly you’re installing a whole testing framework. Yikes...

API Testing: when testing becomes a torture

Most teams already have "API tests" scattered across:

• random curl commands in docs
• unreadable Postman collections everybody is affraid to touch
• a half-finished test suite that only one person understands

LAZYREQUEST makes the happy path boring:

• your requests live in the repo as .http / .rest
• your expectations live next to the request (when you want assertions)
• your terminal becomes the source of truth

A real example (request + expected response)

What If Your .http Files Became Your Test Suite?

That’s exactly what LAZYREQUEST does.

@baseUrl = http://localhost:8080

###
POST {{baseUrl}}/users HTTP/1.1
Content-Type: application/json

{
  "name": "John Doe",
  "email": "[email protected]"
}

###
# Expected response
HTTP/1.1 201 Created
Content-Type: application/json
Location: {{baseUrl}}/users/123

{"ok":true,"id":123}

Then run:

$ lazyrequest

And you instantly know:
✓ Passed/Failed Status
✓ Headers correct
✓ JSON structure correct

Or exactly what failed.

Now your repo contains something you can:

• review in a PR
• run locally
• run in CI
• keep up-to-date without ceremony

Try It. Break It. Star It. ⭐

Installation:

npm i -g lazyrequest

Or clone + build with Bun:

git clone https://github.com/FredySandoval/lazyrequest.git
bun build ./src/index.ts --compile --minify --outfile lazyrequest --target bun

Or download the binary from GitHub releases.
https://github.com/FredySandoval/lazyrequest

npm:
https://www.npmjs.com/package/lazyrequest

Final Thought

The best tools don’t invent new syntax.
They unlock the power of what you’re already using.
LAZYREQUEST doesn’t replace your workflow.
It upgrades it. 🚀

2026-02-25 09:35:28

Mastering AI Agent Memory Architecture: A Deep Dive into Stateful Workflows

As AI agents become more sophisticated, one of the most critical challenges we face is memory management. Unlike traditional software, AI agents need to maintain context, learn from interactions, and adapt their behavior over time. This is where memory architecture becomes crucial.

Why Memory Matters for AI Agents

I've spent countless hours building and refining AI agents, and I've found that memory isn't just about storing information—it's about creating intelligent, stateful workflows. Without proper memory architecture, your AI agent becomes a stateless function, incapable of meaningful interaction.

Here's what good memory architecture enables:

• Context preservation across multiple interactions
• Learning from past conversations
• Maintaining state between tasks
• Personalization of responses
• Efficient retrieval of relevant information

Core Memory Architectures

There are several approaches to implementing memory in AI agents:

1. Vector Databases

For semantic memory, vector databases like Pinecone or Weaviate are invaluable. They store embeddings of text, allowing for efficient similarity search.

from pinecone import Pinecone

pc = Pinecone()
index = pc.Index("ai-agent-memory")

# Store a memory
index.upsert([
    ("memory1", [0.1, 0.2, 0.3], {"content": "The user prefers Python for scripting"}),
    ("memory2", [0.4, 0.5, 0.6], {"content": "User works in data science domain"})
])

# Retrieve similar memories
results = index.query(
    vector=[0.1, 0.2, 0.3],
    top_k=2,
    include_metadata=True
)

2. Graph Databases

For relational memory, graph databases like Neo4j excel at storing connections between entities.

CREATE (user:User {id: "user123"})
CREATE (preference:Preference {type: "language", value: "Python"})
CREATE (domain:Domain {name: "Data Science"})
CREATE (user)-[:HAS_PREFERENCE]->(preference)
CREATE (user)-[:WORKS_IN]->(domain)

3. Key-Value Stores

For simple state management, Redis or DynamoDB work well for storing agent state.

// Set agent state
await redis.set("agent:user123:state", JSON.stringify({
    currentTask: "dataAnalysis",
    lastInteraction: "2023-11-15T14:30:00Z",
    preferences: { outputFormat: "markdown" }
}));

// Get agent state
const state = JSON.parse(await redis.get("agent:user123:state"));

Building a Complete Memory Stack

A robust memory architecture typically combines these approaches:

1. Short-term memory: In-memory cache (Redis) for current session
2. Long-term memory: Vector database for semantic recall
3. Relational memory: Graph database for connections
4. Episodic memory: Time-series database for interaction history

Here's a sample file structure for organizing memory components:

memory/
├── short_term/
│   ├── redis_config.yaml
│   └── session_manager.py
├── long_term/
│   ├── vector_db/
│   │   ├── embeddings/
│   │   └── pinecone_client.py
│   └── graph_db/
│       ├── neo4j_config.yaml
│       └── relationship_manager.py
└── episodic/
    ├── timescale_config.yaml
    └── interaction_logger.py

2026-02-25 09:35:02

Most AI chatbots are pretty bad at support. They hallucinate answers, they can't find relevant information, and they give generic responses that frustrate users more than help them. We know because our first version did all of those things.

We spent the last year building Atlas, the AI assistant inside TicketCord, and the biggest lesson was that the hard part isn't the AI. It's everything around it. The retrieval pipeline, the confidence scoring, the crawling, knowing when to shut up and hand off to a human.

This is how we approached it.

Start with the knowledge base, not the LLM

Everyone wants to jump straight to "hook up GPT and let it answer questions." We tried that. It's terrible. Without grounding the model in actual documentation, it just makes stuff up with confidence.

So we started with the knowledge base. The idea is simple: before you ask an LLM anything, you search your documentation for relevant content and include it in the prompt. This is RAG (retrieval-augmented generation) and it's really the only way to get reliable answers for product-specific questions.

Our knowledge base pipeline works like this:

1. User provides a URL to their documentation site
2. We crawl it
3. We chunk the content into searchable pieces
4. We generate vector embeddings for each chunk
5. We store the vectors in a search engine
6. When a ticket comes in, we search for relevant chunks and feed them to the LLM

Steps 2 through 4 are where most of the complexity lives.

Crawling is harder than it sounds

You'd think crawling a documentation site is simple. Fetch the HTML, parse it, done. In reality about half the sites we need to crawl are JavaScript-rendered SPAs where the initial HTML is basically empty.

We built a dual-mode crawler. First it tries a fast static fetch. If the content looks too short or empty, it falls back to a headless browser that actually renders the JavaScript. This catches React/Next.js/Vue docs sites that render client-side.

The crawler does BFS discovery starting from the root URL. It follows internal links, respects robots.txt, and caps at a configurable depth.

One thing we learned the hard way: you need good content extraction. Raw HTML is full of navigation bars, footers, sidebars, cookie banners, and other junk that pollutes your embeddings. We use a readability algorithm to extract just the main content, with fallback extraction for pages where the primary method doesn't grab enough.

Chunking matters more than you think

Once you have clean text, you need to break it into chunks for embedding. The chunk size has a huge impact on search quality and we went through a lot of iterations to get this right.

The short version of what we learned:

• Too big = bad. Large chunks dilute the embedding. The vector ends up representing too many topics at once and search precision drops.
• Too small = bad. Tiny chunks lose context. You get results that match a keyword but don't have enough surrounding information to be useful.
• Overlap helps. Having chunks share some content at their boundaries preserves context that would otherwise get lost at the split point.
• Respect document structure. Splitting at headings and paragraph boundaries produces much better chunks than splitting at arbitrary character positions. Sentence boundaries are the next best fallback.

We went through about 8 major iterations of our chunking algorithm before landing on something we're happy with. The industry research on optimal chunk sizes was helpful but every use case is a bit different, so you really just have to test with your own data.

Vector search alone isn't enough

Pure vector search (cosine similarity on embeddings) works well for semantic queries like "how do I change the theme color" but poorly for keyword queries like "error code TC007". Embedding models don't understand that TC007 is a specific identifier that needs an exact match.

So we built a multi-stage search pipeline that combines semantic understanding with keyword matching. Without going into too much detail, the general approach is:

1. Semantic search finds results that are conceptually related to the query
2. Keyword scoring boosts results that contain the exact terms the user typed
3. Rank fusion merges both signals into a final ranking
4. Optional re-ranking uses a more expensive model to rescore the top candidates when the initial results are ambiguous

The combination handles both "how does the ticket system work" (semantic) and "error TC007 when closing" (keyword-heavy) much better than either approach alone.

Confidence scoring and knowing when not to answer

This is maybe the most important part. A bad AI answer is worse than no answer at all. If the AI confidently tells a user the wrong thing, they lose trust in the entire system.

Every search result comes back with a confidence score. We set thresholds that determine what happens:

• High confidence: The AI generates a response and shows it with sources
• Medium confidence: The AI shows a draft suggestion to the staff member, not directly to the user
• Low confidence: No suggestion is generated. The ticket gets handled by a human.

The key insight is that "I don't know" is a valid and often correct answer. Most AI implementations skip this step and just always respond, which is how you end up with confidently wrong answers that erode user trust.

We also built a feedback loop where staff can upvote or downvote AI suggestions. This signal feeds back into the search ranking over time. Chunks that consistently produce good answers get a small boost. Chunks that lead to bad answers get penalized. It's simple but it compounds.

Tone analysis and escalation

Beyond answering questions, the AI also monitors the emotional tone of ticket conversations. If a user is getting frustrated or angry, the system can automatically alert a senior staff member or escalate the ticket priority.

We batch messages together before analyzing rather than processing them one by one. This gives better context and saves on API costs.

If the analysis detects escalating negative sentiment, it can ping an escalation role depending on severity. This catches situations where a user is having a bad experience before staff even notice. In communities with hundreds of open tickets at any given time, automated monitoring like this actually makes a difference.

Cache everything that makes sense

AI calls are expensive and slow. We cache aggressively at multiple layers: query embeddings, generated suggestions, search results. The cache TTLs are kept short because support conversations move fast and stale suggestions help nobody.

We also monitor knowledge base freshness. If a documentation source hasn't been re-indexed in a while, the system flags it. Outdated documentation leads to outdated answers, and that's worse than having no knowledge base at all.

What we got wrong

We overengineered the chunking early on. Our first few versions had complex strategies that didn't actually improve results. The simpler approach with well-chosen defaults turned out to be best.

We should have built the feedback loop sooner. The upvote/downvote system was one of the last things we added but it's one of the most valuable. Staff know when an answer is wrong and that signal is incredibly useful.

Cost tracking from day one. AI costs add up fast when you have thousands of bots all making LLM calls. We added token tracking and per-tier limits later but wish we'd built the metering from the start.

The takeaway

If you're building RAG into a product, the retrieval pipeline deserves way more attention than the LLM prompt. A mediocre model with great retrieval beats a great model with bad retrieval every single time. Spend your time on chunking, search quality, and confidence thresholds before you start tweaking prompts.

We're TicketCord. If you want to see Atlas in action, the knowledge base and AI features are available on the Pro plan.

2026-02-25 09:34:22

(This Blog post is part of a collaborative work between Me and Mustapha El Idrissi, Consult his devTo page for more information: https://dev.to/appsbymuss)

Design patterns are proven, reusable solutions to common problems that occur during software design and development. They are like templates that can be adapted to address specific challenges within various contexts. Design patterns are not code themselves but rather descriptions of how to solve problems in a way that maximizes efficiency, scalability, and maintainability.

There is in general 3 types of design patterns:

1. Creational Design Patterns (such as: Singleton Pattern, Simple Factory Pattern)
2. Structural Design Patterns (such as: Decorator Pattern, Facade Pattern, Adapter Pattern, Proxy Pattern)
3. Behavioral Design Patterns (such as: Strategy Pattern, Observer Pattern)

2026-02-25 09:20:26

As of February 25, 2026, the short answer is: move production to Node 24.14.0 LTS first, test Node 25.7.0 in a non-blocking lane, and treat native addons plus framework engine ranges as the main risk surface. Node 24.14.0 and 25.7.0 were both released on February 24, 2026, but 25.x is still the Current line while 24.x is LTS.

The Problem

Teams upgrading Node runtime images in CI often ship regressions from three avoidable gaps:

If you upgrade runtime without a matrix, failures appear as "random" install, test, or startup errors.

The Solution

Start with this risk matrix and rollout order.

flowchart LR
  A[Pin prod to Node 24.14.0 LTS] --> B[Run full CI and integration suite]
  B --> C[Add Node 25.7.0 non-blocking lane]
  C --> D[Fix native rebuild + engines mismatches]
  D --> E[Promote Node 25 lane to required when green]

Runtime deltas that matter in practice

From Node's changelog source files (doc/changelogs/CHANGELOG_V24.md and CHANGELOG_V25.md), two examples with operational impact:

* **http**: add `http.setGlobalProxyFromEnv()`  (#60953)
* **sqlite**: enable defensive mode by default  (#61266)

* **http2**: add `http1Options` for fallback config  (#61713)
* **stream**: rename `Duplex.toWeb()` option to `readableType`  (#61632)

Migration guidance:

• Audit startup/bootstrap code for proxy behavior if you rely on environment-driven outbound traffic.
• Review stream adapters if you typed or wrapped Duplex.toWeb() options.
• Keep sqlite integration tests in CI because 25.7.0 marks sqlite as release candidate and 24.14.0 tightened sqlite defaults.

CI baseline to reduce rollout risk

strategy:
  matrix:
    node: [24.14.0, 25.7.0]
continue-on-error: ${{ matrix.node == '25.7.0' }}

Use this only during rollout; make 25.x required after green stability windows.

Framework compatibility snapshot (verified February 25, 2026)

Interpretation:

• Node 24.14.0 satisfies these common ranges.
• Node 25.7.0 also satisfies ranges, but compatibility still depends on transitive tooling (especially native dependencies in monorepos).

Related playbooks: DDEV CI acceleration and rollout guardrails, PHP 8.4 failure-mode triage in CI, and Secrets governance for runtime safety.

What I Learned

• LTS-first plus Current shadow lane is still the lowest-risk Node upgrade pattern.
• CI breakage risk is now strongly coupled to GitHub Actions runtime policy timelines, not just your Dockerfile.
• Native module risk is mostly a dependency-governance problem; Node-API adoption reduces upgrade friction materially.
• Framework engines checks catch avoidable failures early, but they do not replace full integration tests.

References

• https://nodejs.org/en/blog/release/v24.14.0
• https://nodejs.org/en/blog/release/v25.7.0
• https://raw.githubusercontent.com/nodejs/node/main/doc/changelogs/CHANGELOG_V24.md
• https://raw.githubusercontent.com/nodejs/node/main/doc/changelogs/CHANGELOG_V25.md
• https://github.blog/changelog/2026-02-05-notice-of-upcoming-deprecations-and-breaking-changes-for-github-actions/
• https://nodejs.org/en/learn/modules/abi-stability
• https://github.com/nodejs/node-gyp#installation
• https://github.com/nodejs/release#release-schedule
• https://www.npmjs.com/package/next
• https://www.npmjs.com/package/nuxt
• https://www.npmjs.com/package/@nestjs/core
• https://www.npmjs.com/package/vite
• https://www.npmjs.com/package/express

Originally published at VictorStack AI Blog

2026-02-25 09:17:56

Introduction: Software Determines Whether Architectures Survive

RISC-V is often introduced through the lens of openness. The instruction set is open. The licensing model is open. Hardware implementations are unconstrained by a single vendor. All of this matters. None of it is sufficient on its own.

Architectures survive or fail on software. Not on compilers in isolation, and not on kernel ports alone, but on whether the surrounding software environment can be integrated, maintained, and trusted over time. This only becomes visible once systems move beyond early bring-up and into sustained deployment.

RISC-V is now at that point. It is being evaluated for platforms where software lifetime, verification confidence, and ecosystem stability are not optional considerations. In these contexts, the software ecosystem is not an enabler that will mature later. It is a system-level constraint that shapes programme risk from the outset.

What We Mean by the RISC-V Software Ecosystem

The RISC-V software ecosystem is not a single stack, and it is not owned by any one organisation. It is a collection of layers that evolve at different rates and are maintained by different groups:

• Toolchains and language support
• Operating systems and kernels
• Firmware, runtimes, and middleware
• Debug, profiling, and validation infrastructure
• Platform and enterprise enablement initiatives

This decentralisation is deliberate and is one of RISC-V’s strengths. It enables broad participation and rapid innovation. It also changes where responsibility sits. Integration effort does not disappear simply because components are open. It moves into the system boundary, where assumptions meet reality.

Figure 1: RISC-V ecosystem overview across software architecture and deployment features. Source: ResearchGate

A layered view showing how development tools, operating system support, and system-level capabilities relate to implementation and deployment considerations in RISC-V platforms.

Figure 1 illustrates the layered structure of the RISC-V software ecosystem, showing how toolchains, operating systems, middleware, and application software interact across embedded and enterprise deployments. The separation between layers highlights an important system reality: maturity is uneven. While compilers and operating system support may be usable early, platform-level behaviour, integration constraints, and deployment readiness often emerge later.

Interpreting the ecosystem in this layered way helps engineering teams reason about where integration effort sits and why software enablement must be treated as a system-level concern rather than a single component capability. For engineering teams, the practical question is not whether software exists, but how predictable its behaviour is once components are combined and maintained over time.

Toolchains: Largely Solved, but Not Risk-Free

At the compiler level, RISC-V is well-positioned. GCC (GNU Compiler Collection) and LLVM (Low-Level Virtual Machine) both provide mature backends, and most base ISA configurations are well supported. For many embedded and systems projects, compiler availability is no longer a gating issue.

That does not mean toolchains are irrelevant. Extension combinations, ABI (Application Binary Interface) expectations, and code-generation consistency still matter, particularly when software is reused across silicon variants or suppliers. These issues rarely surface during early development. They tend to emerge later, when implicit assumptions begin to conflict.

Toolchains establish capability. They do not, on their own, guarantee portability or long-term stability.

Operating Systems: Capability Has Outpaced Coherence

Support for operating systems has expanded rapidly. Linux enablement has been a significant milestone, allowing RISC-V platforms to participate in infrastructure-class workloads and to reuse existing software ecosystems. That progress is real and meaningful.

At the same time, Linux availability does not equate to platform uniformity. Firmware interfaces, device descriptions, boot flows, and peripheral assumptions remain highly implementation-specific. These differences are manageable, but they are not free. They require explicit integration effort and ongoing maintenance.

At the embedded and real-time end of the spectrum, multiple RTOS options exist, each optimised for different constraints, certification paths, and lifecycle requirements. Flexibility increases, but predictability decreases unless platform boundaries are clearly defined and enforced.

Middleware and Runtimes: Where Assumptions Collide

Middleware and runtime layers are often where ecosystem fragmentation becomes visible to application teams. Differences in memory models, privilege handling, vector usage, accelerator interfaces, and concurrency assumptions vary between platforms. None of these differences, individually, is problematic. Collectively, they create failure modes that are difficult to diagnose and easy to underestimate.

Portability at the ISA level does not imply behavioural equivalence at the system level. For RISC-V platforms, this distinction must be explicitly acknowledged. Otherwise, integration risk accumulates quietly and is only discovered under load or late in system validation.

Ultimately, ecosystem maturity is experienced by application teams, where inconsistent assumptions surface as friction, delayed ports, or unexpected performance trade-offs.

Enterprise Enablement: Making Gaps Visible Early

As RISC-V adoption moves into commercial and enterprise contexts, the focus shifts from experimentation to predictability. The RISC-V Enterprise Software Ecosystem Dashboard provides visibility into operating system support, tooling availability, and platform readiness across different use cases. Its value lies not in completeness, but in transparency. It makes gaps and dependencies visible early, allowing programme owners to reason about risk before integration begins.

The RISE Project addresses a related challenge. Its focus is not on novelty but on accelerating the availability of production-quality software for commercially relevant RISC-V platforms, particularly Linux-based systems. The existence of the project is itself instructive. It reflects a recognition that organic ecosystem growth, while technically strong, was not converging quickly enough for enterprise adoption timelines.

Neither initiative removes the integration effort. Both make it more explicit and easier to manage.

Verification and Validation: Software Moves the Risk Boundary

Verification strategies often assume that software is a relatively stable input. That assumption holds poorly for emerging platforms. When software stacks are incomplete or inconsistent, faults surface late. Behaviour becomes non-deterministic. Debug effort shifts onto silicon, where visibility is limited, and iteration is slow. At that point, the verification scope expands after schedules have already been committed.

Figure 2: V-Model mapping of verification and validation across system development. Source: MATLAB

A system-level view showing how requirements and design decomposition connect to staged verification and validation during integration, with earlier test loops reducing late-stage risk.

Figure 2 illustrates how verification and validation activities map onto system decomposition and subsequent integration. The key point for RISC-V platforms is not the model itself, but the risk behaviour it exposes. Immature software increases dependence on late integration and system-level testing, where defects are slower to isolate and more expensive to resolve. Pulling representative software, firmware, and toolchain assumptions into earlier verification loops reduces late-stage churn and improves programme confidence before silicon debug becomes the default path.

For RISC-V platforms, adequate verification increasingly depends on early hardware–software co-development. Software-visible behaviour must be modelled explicitly. Toolchains, kernels, firmware, and platform assumptions need to be exercised together, not sequentially.

If this discipline is absent, programme risk does not disappear. It migrates from hardware into software, often without any corresponding adjustment to schedules, resourcing, or verification scope.

Governance, Profiles, and Long-Term Stability

Open governance is one of RISC-V’s defining characteristics. It enables broad participation and reduces vendor lock-in. It does not, by itself, ensure stability. Software longevity depends on clearly defined profiles, ABI stability, compliance expectations, and managed evolution. These mechanisms are still maturing, as reflected in RISC-V International’s documentation of its software ecosystem. Progress is tangible, but uneven across domains.

One practical challenge for programme owners is that platform decisions often need to be frozen while parts of the ecosystem continue to evolve. The need to freeze platform decisions while parts of the ecosystem continue to evolve increases the importance of clearly defined baselines and explicit assumptions, particularly for long-lived or regulated systems.

Ecosystem maturity should therefore be treated as an engineering variable rather than an assumption.

What Matters When Evaluating Readiness

When engineering teams assess the RISC-V software ecosystem, the most valuable questions are rarely about feature lists:

• Which layers are stable enough to depend on
• Which assumptions are implicit rather than documented
• Where integration responsibility actually sits
• How change propagates through verification and validation flows

The answers to these questions determine whether RISC-V delivers genuine architectural control or simply redistributes complexity across the programme.

Continue Exploring

Advance from Ecosystem Insight to Practical Verification
If this article has highlighted how software assumptions influence verification risk, our 3-Part RISC-V Verification Course (Live Online, 9 March–21 April 2026) provides the structured, hands-on depth needed to apply best-practice CPU and SoC verification in real projects — covering architectures and microarchitectures, ISA and toolchains, riscv-dv instruction stream generation, CPU integration, SoC feature verification, debug, coverage, and sign-off — combining lectures, quizzes, and practical exercises to translate ecosystem insight into confident execution.