DEV Community: chowderhead

JEV assisted LLM Trading

chowderhead — Sun, 20 Sep 2026 19:16:39 +0000

We gave our LLM trading bot an auditor that never sees the chart

Every hour, on the hour, a vision model looks at three forex charts and decides whether our bot should buy, sell, or sit out. It has been doing this with real money since May. As of last Saturday, something finally grades its reasoning.

This post is about the grader.

The bot, briefly

It's a Python script called gemini_snap_flash. Once an hour it sends a one-minute candlestick chart plus daily and weekly context images to Gemini, gets back UP or DOWN and a one-line thesis, and trades EURUSD, GBPUSD, or USDJPY through MT5 on funded prop accounts. One position per pair, fixed stop and take-profit, and hard rails around it: 2% daily loss cap, 0.6% daily profit cap, and an external watchdog that kills the process after four losses in a row.

The rails matter. The first $10k account blew through its max drawdown on September 9 and is gone. The fleet is two $100k accounts now, run as separate instances with separate state.

Here's the part most AI-trading posts skip. The bot already logs a lot. signal_log.csv has every decision, executed or skipped, with the model's reasoning attached. trade_analysis.csv has every close with MFE/MAE in pips and R-multiples. What nothing had was a check on the decision itself: does the thesis actually argue for the direction the model picked? When the account died, I could read every trade and every excuse, and none of it told me whether the reasoning was any good.

What JEV is

JEV is TypeSafe's System-One model (jev-latest), served at api.typesafe.ai/v1/systemone. It's text-only. No vision, no market opinions. You POST it a "state" string plus a set of questions, each with instructions and pass/fail criteria, and it returns structured verdicts:

{
  "state": "<the thing being judged>",
  "model": "jev-latest",
  "questions": {
    "shadow_direction_consistency": {
      "type": "noul",
      "instructions": "Based only on the provided state (a recorded hourly vision decision for a live FX scalper), is the recorded directional call a clear, self-consistent directional commitment coherent with its own thesis?",
      "criteria": {
        "true": "The recorded direction is explicit (UP or DOWN) and the recorded thesis is coherent with and supports it.",
        "false": "The direction is ambiguous, contradicts the thesis, or no clear directional commitment is recorded."
      }
    }
  }
}

The shape isn't an accident. A noul question is a pass/fail check the model answers one way against criteria you wrote — not an open-ended essay. That's what makes the verdict usable as a watchdog: it stays binary and countable, so you can line thousands of them up against the trades they judged and watch the incoherence rate move. An essay grader couldn't do that; it would drown you in prose and give you nothing to plot.

So it's a proofreader, not a fortune teller. It can't tell you whether UP is right about EURUSD. It can tell you when UP arrives with a thesis that quietly argues DOWN.

The shadow harness

The new file is shadow.py, about 430 lines, plus tests. The design has one hard rule: the trading path must not know it exists unless we say so.

Everything is gated behind SHADOW_MODE=1. When that's unset, the harness is a complete no-op; the only cost on the hot path is a single os.getenv() call, and the tests assert that zero network calls happen. When it's on, for every hourly vision decision the harness:

Dumps the exact chart images that went to the vision model into shadow_out/<instance>/<timestamp>/, and records a sha256 of the prompt text for later lookup.
Runs exactly one JEV call over a text rendering of those same inputs, hard-timeboxed at 20 seconds on a daemon thread. The state is capped at 30k characters to stay inside the model's context.
Appends one JSON line per decision to shadow_log.jsonl, including the JEV verdict and its latency.

A little later, an attach_outcome() call patches the most recent unfinalized line for that pair with what actually happened: EXECUTED, FAILED, or ORDER_FAILED, plus the ticket. It only touches lines written in the last 15 minutes, so a slow hourly cycle can't corrupt an older decision's record.

Each instance writes to its own log, so the fleet's accounts never mix.

The switch is currently off

Worth saying plainly: SHADOW_MODE is not set in any launch config yet, so zero shadow calls have happened in production. The dashboard probe ledger shows nine calls, all from us poking at the API while building the plugin, and the Shadow Harness tab reads NOT ENABLED until the first row lands. The harness is new and the tests are new, and we want a few market-open cycles of confidence before turning on a collector that sits in the decision path.

What we expect the data to answer

Three questions, in order of interest.

First, the big one: does coherence predict money? Every verdict lands in the same record as the real action and, downstream, the real P&L. If incoherent trades lose measurably more often than coherent ones, JEV graduates from observer to entry filter, and we skip or downsize flagged decisions. If it doesn't, we drop it. That's the point of measuring instead of believing.

How a verdict gets scored is fixed before the first data point. The pass/fail bar is pre-registered in code — minimum sample sizes, a minimum counterfactual edge, a ceiling on JEV errors — and the backend and the dashboard banner read the same constants, so the goalposts can't move after the data arrives. That same tab is where we watch it: it shows one of five states — NOT ENABLED, INSUFFICIENT, SHIP CANDIDATE, KILL, HOLD — next to the histogram of disagreement edges and a per-day series. The edge itself is computed counterfactually: on rows where JEV withholds endorsement, we flip the sign of what the vision-side trade actually realized. That's a mirror approximation, not a P&L replay, and it's labelled as one; orders that never resolved are counted separately, never scored as zero. And if JEV's disagreements turn out to correlate with winners, the shadow signal dies first.

Second, drift detection. The bot retries Gemini up to six times with exponential backoff, and we've swapped models before (3.5-flash to 3.6-flash in July). Degraded or fallback responses usually get sloppier before they get unprofitable. A rise in inconsistent verdicts is a cheap alarm — a shadow call is text-only, input-only pricing, a fraction of a cent per decision, and every record carries the JEV latency and error status, so degradation shows up in the record itself before the drawdown alert does.

Third, model comparison. Since the harness records each prompt's hash and the decision but stays agnostic about which model produced them, the same record can score an OpenRouter alternative against Gemini on identical charts. Evidence beats vibes when picking a model.

What it can't do

The limits are real and worth naming. JEV sees the text of the decision, never the chart pixels, so it audits self-consistency only. It will happily bless a confident, well-written, wrong call. It evaluates after the fact, so today it can't stop a trade, which is deliberate: any gating step is a later change with its own risk to review. And it's one question right now. Coverage is only as good as the question set, and one question is a start, not a panel. The API shape makes a panel cheap, though: several independent pass/fail checks can ride in a single request and get combined in code, and our dashboard probe already does that. Adding questions to the harness is a config change, not a rewrite.

The whole thing is built to answer one question, and the answer is allowed to be no. If coherent trades don't make more money than incoherent ones, JEV gets dropped and we've lost a few cents a decision and a couple of weeks. If they do, the auditor stops being a proofreader and becomes a filter. Either way the data decides, and the bar was written before the first verdict landed. That's the part I'm actually proud of — not the harness, the willingness to let it be wrong.

Engram: A new type of AI

chowderhead — Tue, 24 Mar 2026 21:05:54 +0000

From the author:

From my endless pursuit of getting LLM's to stop hallucinating I had a late night chat with gemini discussing what if's.

What if we could use a vector db during training, what if we could allow for agentic reasoning at the point of training.

What if the LLM could apply this reasoning between layers?

I had my ai-agent code up a prototype and started training it only to be disappointed with word salad.

I think I'm on to something here though-

I recently came across a paper by deepseek, and it aligns with the ideas I had , I mean I even called my repo Engram - and this is an idea discussed in their paper

coiencidence ? i think not haha

But I need help figuring out training- Whats the best way to train this thing?

Consider this less a how-to article, but more asking for help to keep pushing this thing forward- I think i'm on to something.

*Word salad: *

The code: https://github.com/kent-ai-dev/engram

⏺ How Engram Actually Thinks: A Deep Dive Into Its Reasoning Engine

The Attention Brain: A Panel of Pattern-Matching Judges

Think of Engram's reasoning engine like a panel of 4 judges at a talent show. Each judge watches the same performance (your input text), but they're looking for different things. Judge 1 notices basic patterns. Judge 2 spots relationships. Judge 3 identifies abstract themes. Judge 4 makes the final call.

That's what the 4 stacked attention layers do.

Here's the thing though: these aren't independent judges. They're more like a relay race. Judge 1 passes their observations to Judge 2, who adds their own analysis and passes it forward. By the time it reaches Judge 4, the understanding is way more sophisticated than what Judge 1 saw.

But what does "understanding" even mean for a neural network? It's just numbers. Lots and lots of numbers.

Words Are Coordinates in Space (Yes, Really)

Before we can talk about reasoning, we need to understand how Engram sees words. It doesn't see letters or syllables. It sees coordinates.

Every word in Engram's vocabulary is a point in 96-dimensional space. I know that sounds like sci-fi nonsense, but it's simpler than it seems. You can't visualize 96 dimensions, so let's pretend it's 3D space for a second.

Imagine "cat" is at position (5, 2, 8). "Dog" might be at (5.3, 2.1, 7.8) — really close to "cat" because they're both small furry pets. "Car" might be way over at (45, 12, 3) because it's got nothing to do with animals.

Words with similar meanings cluster together in this space. Words that appear in similar contexts are neighbors. That's how the model "understands" that "cat" and "dog" are related — they're literally close together in math-space.

So when you type "the cat sat on the", Engram converts that into 5 points in 96D space. Those are the input coordinates.

Attention: The Smart Way to Look Backwards

Now here's where it gets interesting. When the brain is trying to predict what comes after "the cat sat on the", it can't just look at the last word. That's useless. "The" tells you almost nothing.

It needs to look back at the whole phrase and ask: "Which earlier words matter most right now?"

That's attention. It's a weighted search through previous context.

Let's break down how one attention layer works. There are three mini-operations happening inside:

Query, Key, Value (The Spotlight Mechanism)

Every word gets transformed into three different representations:

Query: "What am I looking for?"
Key: "What do I have to offer?"
Value: "What information should I give you?"

When predicting after "on the", the query is asking: "I need a location noun." Then it scans backward through all the previous words' keys. "Cat" offers "animal subject." "Sat" offers "past tense verb." "On the" offers "preposition indicating spatial relationship."

The attention mechanism computes scores: how relevant is each previous word to what I'm looking for right now? It's like shining a spotlight on certain words while dimming others.

Then it takes a weighted average of the values. If "cat" got a high attention score, its value (the actual information extracted from it) gets weighted heavily in the final output.

Here's the math, but don't panic:

attention_score = (query · key) / sqrt(96)
attention_weights = softmax(attention_scores)
output = weighted_sum(values, attention_weights)

Translation: multiply how much you're looking for by what each word offers, convert to percentages, take weighted average.

That's it. That's attention. It's just a smart way to focus on relevant context.

The Feedforward Network (The Actual Thinking Part)

After attention gathers information from previous words, there's a second step: the feedforward network. This is where computation happens.

It's a simple two-layer neural network:

Expand the representation into a bigger space (4x larger)
Run it through a nonlinear activation function (GELU, which lets the network learn complex patterns)
Compress it back down to the original size

Why expand then compress? Because the bigger space gives the network room to explore different interpretations. It's like brainstorming on a whiteboard before writing your final answer.

The feedforward network learns things like "if I see [preposition + determiner] pattern, there's usually a noun coming next" or "question words at the start change the entire sentence structure."

Residual Connections (The Memory Trick)

There's one more detail that matters: residual connections. After each attention block and feedforward block, the output gets added back to the input.

Why? Because neural networks are bad at remembering their starting point after many transformations. Residual connections let information skip forward unchanged, so the network doesn't forget what the original input was.

It's like taking notes during a lecture. You don't just memorize the professor's summary at the end — you keep your original notes too.

The 4-Layer Journey: How Understanding Deepens

Let's trace what happens when you input: "what is the capital of"

Layer 0 processes the raw word vectors. Attention identifies that "what" and "capital" are the important words. "Is" and "the" get low attention scores. Feedforward recognizes this as a question pattern. Output: "This is asking for a capital city of something."

But wait. Before Layer 1 starts, something new happens.

N-gram Memory Injection (The Shortcut)

This is where Engram diverges from standard transformers. After Layer 0 finishes, the brain looks up bigrams and trigrams in a hash table:

Bigram: "capital" + "of" → hash(47821) → table entry #3821 → embedding vector
Trigram: "the" + "capital" + "of" → hash(91204) → table entry #1204 → embedding vector

These vectors represent "how often this phrase appears in the training data and what usually follows it." It's pre-computed pattern recognition. No thinking required.

But here's the clever part: a learned gate decides how much to trust this memory. The gate looks at Layer 0's output and the N-gram memory and computes a blend weight.

If the model is confident the N-gram memory is relevant, it injects it heavily. If it's uncertain, it uses less. This happens between Layer 0 and Layer 1, so the remaining layers get a head start.

Why does this matter? Because reconstructing common phrases wastes neural computation. Layers 1-4 can focus on actual reasoning instead of re-deriving "capital of → country name" from scratch every time.

Layer 1 receives the memory-enhanced representation. It refines the understanding: "capital of → probably expecting a country name, but could be a state."

Layer 2 goes deeper: "question structure → expects a proper noun answer, geographic domain."

Layer 3 adds context: "no other geographic context in the sentence → this is a standalone geography quiz question."

Layer 4 makes the final prediction: a 96D vector representing "the concept of a country name, probably European or well-known."

Each layer makes the representation more abstract and more task-specific.

Pondering: Thinking Harder When Confused

Most neural networks run through their layers once and call it done. Engram doesn't.

After the initial pass through Layers 1-4, a halt gate checks: "Am I confident enough, or should I think more?"

It's a tiny neural network (literally one weight matrix) that looks at the final output and predicts a confidence score. If the score is high (above 0.95), it stops. If it's medium or low, it loops back through the layers again.

This can happen up to 3 times. Each loop refines the prediction more.

Easy inputs (common phrases, simple patterns) usually halt after 1 pass. Hard inputs (rare words, complex grammar) ponder for 2-3 passes.

It's adaptive compute. The model literally allocates more processing power to difficult problems.

GPT-4 doesn't do this. It runs through all 96 layers for every token, whether you're typing "hello" or asking it to prove the Riemann hypothesis. Same compute budget for everything.

Engram uses 1x compute for "hello" and 3x compute for weird philosophical questions. That's the pondering mechanism in action.

The Final Prediction: From Vector to Word

After pondering finishes, the brain outputs a 96-dimensional vector. Let's call it prediction_vector.

This vector represents "the abstract concept of what should come next." It's not a word yet. It's a coordinate in concept space.

Now the brain searches ChromaDB (the vocabulary database) for the word whose vector is closest to prediction_vector. It measures distance using L2 norm (Euclidean distance).

Candidate words:

"france" → distance 0.23
"paris" → distance 0.31
"germany" → distance 0.38
"banana" → distance 2.45

"France" is closest, so it's the top candidate. But the model doesn't just pick the closest one. That'd be boring and repetitive.

Instead, it samples from the top-k candidates using temperature. Temperature controls randomness:

Low temp (0.1): almost always picks "france" (safe, predictable)
High temp (1.5): might pick "paris" or "germany" sometimes (creative, risky)

Engram defaults to 0.9 — a balance between coherence and variety.

Episodic Memory: The Experience Boost

Before finalizing the answer, there's one more step. If episodic memory exists (it does in test_brain.py), the model queries the memory database.

It searches for past interactions where the brain's internal state was similar to right now. Not similar words — similar brain states. The search uses the prediction vector as the query.

Let's say it finds: "Three conversations ago, user asked about European capitals, and the answer was France."

It retrieves those memory vectors and blends them into the current prediction using the same learned gate that was used for N-gram memory.

If the gate decides the memory is relevant, it shifts the prediction slightly toward the remembered answer. If the memory seems irrelevant, the gate suppresses it.

This is how Engram "remembers" past conversations without explicitly storing facts in its weights.

Why This Architecture Is Different

Standard transformers (GPT, BERT, etc.) are pure attention stacks. Information flows in one direction: input → layer 1 → layer 2 → ... → layer N → output. Fixed depth, fixed compute, no external memory.

Engram adds three things on top of the standard transformer:

Conditional memory lookup (N-gram tables injected between layers)
Adaptive depth (pondering mechanism, 1-3 loops based on confidence)
External episodic memory (brain-state-indexed experience journal)

And it learns differently too. When the model makes a prediction error, it computes surprise:

surprise = |predicted - actual|²

If surprise is high (the model was really wrong), it multiplies the gradient by up to 3x. If surprise is low (the model already knew this), it barely updates.

This is dopamine-style learning. Humans learn faster from shocking events than from boring repetition. Engram does the same.

The Whole Pipeline, Start to Finish

You type: "what is the capital of france"

Tokenize into words: ["what", "is", "the", "capital", "of", "france"]
Look up word vectors in ChromaDB (96D coordinates for each)
Add positional embeddings (so the model knows word order)
Layer 0 attention: focus on "what" and "capital"
Layer 0 feedforward: recognize question pattern
N-gram memory injection: look up "capital of" in hash table, gate decides relevance, inject
Layer 1-4: refine understanding through attention + feedforward
Halt gate check: confident? Yes → stop. No → loop Layers 1-4 again (up to 3 times)
Episodic memory query: search for similar past brain states, retrieve, gate, blend
Nearest-word search: find closest word vector in vocab ("paris" or "france" depending on context)
Temperature sampling: randomly pick from top-k candidates
Output: "paris"

That's the full reasoning pipeline. Four layers of attention, memory lookups at two different points, adaptive pondering, and context-aware gating throughout.

What Makes This Research-Worthy

Big companies don't build models like this. They scale transformers to trillions of parameters and call it done. Engram explores orthogonal ideas:

Can you separate reasoning from vocabulary? Yes — ChromaDB proves it.
Can you inject memory between layers instead of compressing it into weights? Yes — N-gram tables prove it.
Can you adaptively allocate compute based on input difficulty? Yes — pondering proves it.
Can you learn from surprise instead of treating all data equally? Yes — surprise-weighted gradients prove it.

None of these ideas require massive scale. They work at 900K parameters. That's the point.

Engram isn't trying to beat GPT-4. It's trying to answer: "What if we treated memory and computation as separate, complementary systems instead of mashing everything into one giant neural blob?"

The DeepSeek paper (Jan 2026) validated this approach at scale. Their Engram module (same name, independent discovery) showed that memory injection gives reasoning gains that exceed knowledge gains. Layer 5 with memory matches Layer 12 without it.

That's not incremental improvement. That's architectural rethinking.

So yeah. That's how Engram thinks. Attention to focus on context. Feedforward to compute transformations. Memory to shortcut common patterns. Pondering to think harder when needed. Gating to decide what to trust.

It's a reasoning engine built from interpretable parts. You can trace every step. You can modify every piece. And you can actually understand what's happening under the hood.

That's rare in modern AI. Most models are black boxes. Engram is a glass box.

Trust the Server, Not the LLM: A Deterministic Approach to LLM Accuracy

chowderhead — Fri, 28 Nov 2025 16:16:03 +0000

Updated: Friday, OCT 16 2026

Zero Mental Math: every number is computed by code, rendered by code, and checked by code. The model writes commentary, and its commentary ships only if a deterministic verifier passes it.

🎯 The problem

I built an MCP server that pulls data from my MT5 terminals on a VPS: trades, positions, averages, technical indicators — basically a stack of financial reports I can query in plain English.

Once it was wired up, the model started inventing things. My favorite: it reported a 16th trade on a day that had 15. In financial reporting, "mostly right" is wrong, so I went through the recent research on tool use, RAG and verification and rebuilt the pipeline around one rule: the model never produces a number, and nothing it writes reaches me without being checked by code.

This post is the design, the reference code, what it guarantees, and — just as important — what it doesn't. I wrote this intro; the rest was drafted with AI help, and every number and output shown below came from running the code in this post.

TL;DR

Compute every metric in the tool server (Python, Decimal for money). Raw rows never reach the model.
Render the facts block with code. The model doesn't copy numbers into a template — it never touches that block.
Verify the model's commentary with code: every number must exist in the tool payload, every strategy reference must match an exact lookup table.
Retry once with the verifier's issue list as feedback. If it still fails, ship the facts without commentary.
The guarantee is narrow and real: what you read was computed from the data the tool queried, and delivered commentary contains no number or strategy the payload doesn't. It does not cover the model calling the wrong tool, querying the wrong period, or drawing the wrong conclusion from correct numbers.

⚠️ Why models get numbers wrong

Exact arithmetic is still unreliable in 2026. Frontier models win math olympiad medals, yet a February 2026 Google study found that every frontier model it tested without tools — including Claude Opus 4.1, GPT-5 and Gemini 2.5 Pro — loses accuracy on plain two-number integer addition as the operands get longer, with most errors traceable to misaligned digits or mishandled carries [1]. The configuration that held up was a model with code execution, and its remaining errors came from skipping the tool or miscopying the tool's answer. That is this whole post in one result: compute in code, then check the copy.

My failure wasn't arithmetic, it was aggregation in context. The 16th trade came from the model counting rows in a long tool response. Models don't use long inputs uniformly — retrieval accuracy depends on where the relevant item sits in the context [2]. Handing a model 400 deal rows and asking "how many positions?" is asking it to aggregate in its head.

Learned arithmetic is fragile, not impossible. The classic result here is that how numbers are tokenized matters enormously: with explicit position tokens a T5 model learned to add and subtract numbers up to 60 digits, but no model learned rules that extrapolated past the lengths it was trained on [3]. Fragile and length-bound is still not something to put between a trade log and a P&L figure.

Temperature 0 doesn't make an endpoint deterministic — and determinism isn't accuracy anyway. Most inference endpoints are nondeterministic mainly because your request gets batched with whatever else is in flight, batch size changes with load, and common kernels aren't batch-invariant [4]. But even a perfectly deterministic model can repeat the same wrong number every time. What you need is a check, not reproducibility.

🏗️ Architecture

user question (plain English)
        │
        ▼
LLM picks tool + typed params ────────────────  not verified (see "The boundary")
        │
        ▼
L1  tool computes metrics (Python, Decimal)        deterministic
L2  payload sealed under a snapshot id              deterministic
L3  facts block rendered from payload               deterministic
L4  entity table (dict / SQL) ───────────┐          deterministic
        │                                 │
        ▼                                 ▼
LLM drafts commentary ──► L5 verify(text, payload, entities)   deterministic
        ▲                         │ issues
        └──── L6 one retry ◄──────┘ → then fail closed: facts only

Trust flows from deterministic systems to stochastic ones, never the reverse. The model can propose; only code can approve.

🔧 Layer 1: Fat tool server (compute everything)

The MCP tool returns metrics, never raw rows that would require the model to count, sum or divide.

from decimal import ROUND_HALF_UP, Decimal

CENT = Decimal("0.01")


def q(x: Decimal) -> Decimal:
    return x.quantize(CENT, rounding=ROUND_HALF_UP)


def position_summary(pnls: list[Decimal]) -> dict:
    if not pnls:
        raise ValueError("no closed positions in range")
    wins = [p for p in pnls if p > 0]
    losses = [p for p in pnls if p < 0]  # break-even trades count toward the total only
    gross_win, gross_loss = sum(wins, Decimal(0)), -sum(losses, Decimal(0))
    n = len(pnls)
    return {
        "total_positions": n,
        "total_wins": len(wins),
        "total_losses": len(losses),
        "win_rate_pct": q(Decimal(100 * len(wins)) / n),
        "total_pl": q(gross_win - gross_loss),
        "avg_win": q(gross_win / len(wins)) if wins else None,
        "avg_loss": q(-gross_loss / len(losses)) if losses else None,
        "profit_factor": q(gross_win / gross_loss) if gross_loss else None,
        "expectancy": q((gross_win - gross_loss) / n),
    }

A few details that matter more than they look:

Decimal, not float, for money. Rounding is explicit and identical everywhere.
Undefined is None, not a made-up number. Profit factor with zero losing trades is undefined; the renderer prints n/a.
Echo the resolved query. The payload carries {"from": "2025-11-01", "to": "2025-11-30", "timezone": "UTC", ...} so the reader sees exactly what was queried.
Make the payload a contract. MCP tools declare an inputSchema, and since the 2025-06-18 spec can declare an outputSchema and return typed structuredContent [5]. Use enums for accounts and symbols and a date format for ranges, so a malformed call fails at the boundary instead of producing a plausible report.

This is program-aided reasoning [6] in production form, and the tool-use framing comes from Toolformer [7] and ReAct [8]: delegate what models do poorly to code.

Testing: unit-test each tool against a fixture whose values you checked by hand. A deterministic function needs one correct run per case, not repeated runs — rerunning it only proves it's deterministic, not that it's right.

📝 Layer 2: Provenance (seal the payload)

Every payload gets a snapshot id, the facts block displays it, and the verifier checks commentary against that exact payload.

import hashlib
import hmac
import json


def snapshot_id(payload: dict, key: bytes) -> str:
    canonical = json.dumps(payload, sort_keys=True, separators=(",", ":"), default=str)
    return hmac.new(key, canonical.encode(), hashlib.sha256).hexdigest()


def snapshot_ok(payload: dict, key: bytes) -> bool:
    body = {k: v for k, v in payload.items() if k != "snapshot"}
    return hmac.compare_digest(snapshot_id(body, key), payload["snapshot"])

Use the right tool for the job:

Same process? If the verifier runs inside the server that produced the payload, the id is just a lookup key — a UUID works.
Crossing a trust boundary? If the payload passes through a client, a queue or another machine before verification, use HMAC-SHA256 [9] over canonical JSON, with the key loaded from the environment. Changing any metric invalidates the seal.
Not CRC32. CRC32 detects accidental corruption; it's linear, and collisions are easy to construct deliberately, so it offers no tamper resistance.
Never ask the model to echo a checksum as proof. A model can append a correct tag to a response with a wrong number in the middle. The tag proves the model saw the payload, not that it used it correctly.

Citations alone don't establish accuracy either: a human audit of commercial generative search engines found only about half of generated sentences were fully supported by their citations, and about a quarter of citations didn't support the sentence they were attached to [10]. Provenance is useful because it tells the verifier what to compare against — the comparison itself is Layer 5.

📄 Layer 3: Deterministic rendering

The facts block is rendered by Python. The model doesn't fill template slots with copied citations; it never edits this block at all.

def money(x: Decimal | None) -> str:
    if x is None:
        return "n/a"
    return f"-${-x:,.2f}" if x < 0 else f"${x:,.2f}"


def render_facts(p: dict, strategies: dict[int, str]) -> str:
    s, lots, period = p["summary"], p["lots"], p["query"]
    change = lots["largest_change"]
    pf = s["profit_factor"] if s["profit_factor"] is not None else "n/a (no losing trades)"
    return "\n".join([
        f"## Performance — {strategies[p['magic']]} [magic {p['magic']}]",
        f"Period: {period['from']} → {period['to']} ({period['timezone']}) · "
        f"snapshot `{p['snapshot'][:12]}`",
        "",
        f"- Positions: {s['total_positions']} ({s['total_wins']} wins, {s['total_losses']} losses)",
        f"- Win rate: {s['win_rate_pct']}%",
        f"- Total P&L: {money(s['total_pl'])}",
        f"- Average win / loss: {money(s['avg_win'])} / {money(s['avg_loss'])}",
        f"- Profit factor: {pf}",
        f"- Expectancy per position: {money(s['expectancy'])}",
        f"- Lots, last {len(lots['recent'])}: {', '.join(map(str, lots['recent']))} "
        f"→ current {lots['current']}, mean {lots['mean']}",
        f"- Largest lot change: {change['from']} → {change['to']} "
        f"({change['pct']:+}%) on {change['date']}",
    ])

Output for the sample month used throughout this post (29 positions on XAUUSD):

## Performance — Goldfish Scalper [magic 106]
Period: 2025-11-01 → 2025-11-30 (UTC) · snapshot `00a395f9721c`

- Positions: 29 (19 wins, 10 losses)
- Win rate: 65.52%
- Total P&L: $956.30
- Average win / loss: $88.63 / -$72.76
- Profit factor: 2.31
- Expectancy per position: $32.98
- Lots, last 5: 0.05, 0.05, 0.05, 0.08, 0.07 → current 0.07, mean 0.06
- Largest lot change: 0.05 → 0.08 (+60.00%) on 2025-11-24

Structure is not semantics. If you want structured output from the model itself — say, JSON with a commentary field — use constrained decoding. The idea goes back to lexically constrained beam search [11]; today it means JSON-schema-constrained generation, which constrains the shape of the output (and frameworks differ in how much of JSON Schema they actually support) but says nothing about whether its contents are true [12]. Strict format constraints can also measurably degrade a model's reasoning [13]. Constrain format where you need to parse; verify content separately.

🗄️ Layer 4: Exact entity grounding

Not every hallucination is numeric. The model can report the right win rate and attribute it to the wrong strategy.

A magic number is an exact integer key, so it gets an exact lookup:

STRATEGIES = {106: "Goldfish Scalper", 214: "Dark Dione"}  # or a SQL table

The renderer always writes strategies as Name [magic N], and the commentary prompt requires the same form, which gives the verifier something unambiguous to check.

Vector search is the wrong tool for exact IDs: embedding similarity is approximate by design and adds retrieval noise and tokens to a lookup that a dict answers exactly. Retrieval-augmented generation [14] earns its place for fuzzy, prose-shaped knowledge — trading rules, playbooks, notes — never for ID-to-name mapping.

✅ Layer 5: Deterministic verifier

This is the gate. It runs on every draft and needs no model.

import re

NUMBER = re.compile(r"(?<![\w.])-?\$?\d[\d,]*(?:\.\d+)?%?")
HEDGE = re.compile(r"(?:~|\b(?:approx(?:imately)?|about|around|roughly|nearly|almost)\s+)\$?\d", re.I)
TAG = re.compile(r"\[magic (\d+)\]")


def leaves(obj):
    if isinstance(obj, dict):
        obj = list(obj.values())
    if isinstance(obj, (list, tuple)):
        for v in obj:
            yield from leaves(v)
    else:
        yield obj


def verify(text: str, payload: dict, strategies: dict[int, str]) -> list[str]:
    issues = []
    # Entities: every [magic N] tag must sit right after N's name, and no other strategy may appear.
    for m in TAG.finditer(text):
        name = strategies.get(int(m.group(1)))
        if name is None:
            issues.append(f"unknown magic number {m.group(1)}")
        elif not text[:m.start()].rstrip().endswith(name):
            issues.append(f"[magic {m.group(1)}] belongs to {name!r}")
    for magic, name in strategies.items():
        if name in text and magic != payload["magic"]:
            issues.append(f"{name!r} is not part of this report")
    # Numbers: after removing tags and payload strings (dates, symbols), every number must be in the payload.
    prose = TAG.sub("", text)
    for s in (v for v in leaves(payload) if isinstance(v, str)):
        prose = prose.replace(s, "")
    allowed = {abs(Decimal(v)) for v in leaves(payload) if isinstance(v, (int, Decimal))}
    for token in NUMBER.findall(prose):
        value = abs(Decimal(token.replace("$", "").replace(",", "").rstrip("%")))
        if value not in allowed:
            issues.append(f"unsupported number: {token}")
    if HEDGE.search(prose):
        issues.append("approximation language: report exact figures or none")
    return issues

A bad draft:

Goldfish Scalper [magic 106] had a strong month: roughly 70% of trades won and P&L reached $1,350.00. Dark Dione [magic 106] sized up 0.05 → 0.08 lots.

- [magic 106] belongs to 'Goldfish Scalper'
- 'Dark Dione' is not part of this report
- unsupported number: 70%
- unsupported number: $1,350.00
- approximation language: report exact figures or none

A good draft passes with zero issues:

Goldfish Scalper [magic 106] won 19 of 29 positions. The average win of $88.63 is larger than the average loss of $72.76, which is why expectancy stays positive at $32.98. Size rose 60.00% on 2025-11-24 and is currently 0.07 lots, above the 0.06 mean.

It fails closed. A false positive withholds commentary; it never alters a number.

Know its limit. It checks that every number exists in the payload, not that it's attached to the right label — "win rate 72.76%" would pass, because 72.76 is the average loss. If you need pairing covered too, use strict mode: the model writes {summary.avg_win} and code writes the digits.

SLOT = re.compile(r"\{([\w.]+)\}")


def fill_slots(commentary: str, payload: dict) -> str:
    if NUMBER.search(SLOT.sub("", TAG.sub("", commentary))):
        raise ValueError("literal number in strict-mode commentary")

    def value(m):
        node = payload
        for key in m.group(1).split("."):
            node = node[key]  # unknown slot -> KeyError -> reject the draft
        return str(node)

    return SLOT.sub(value, commentary)

In strict mode the model is never in a position to type a digit, so there is nothing to miscopy.

Where a model judge still helps

Some claims aren't numeric: "risk management changed this week," "the edge looks like it's decaying." No regex checks those. If you add an LLM judge for them, keep it advisory and keep it away from the numbers:

Use a different model family from the generator. LLM judges carry known biases, including favoring their own outputs [15]; that self-preference tracks how well a model recognizes its own text [16].
Don't assume checking is easier than writing. Verification reliability depends on problem difficulty and generator strength, and errors from strong generators are harder for verifiers to catch [17]. LM judges and reward models still fall well short of a perfect verifier; combining several of them narrows that gap [18].

For numeric claims, comparing against the payload is the perfect verifier. Use it.

🔄 Layer 6: Bounded repair, fail closed

def build_report(payload, strategies, draft_commentary, max_attempts=2) -> dict:
    facts = render_facts(payload, strategies)  # the model never edits this block
    issues: list[str] = []
    for attempt in range(1, max_attempts + 1):
        commentary = draft_commentary(facts, issues)  # the only model call
        issues = verify(commentary, payload, strategies)
        if not issues:
            return {"report": f"{facts}\n\n{commentary}",
                    "verified": True, "attempts": attempt}
    return {"report": f"{facts}\n\n_Commentary withheld: failed verification._",
            "verified": False, "attempts": max_attempts, "issues": issues}

With the bad draft first and the good draft second: {"verified": true, "attempts": 2}. With a model that keeps failing, you still get every number — the report just ends in _Commentary withheld: failed verification._

Why it's shaped this way:

The feedback is external ground truth. The retry prompt carries the verifier's exact issue list. Without external feedback, models struggle to correct their own reasoning and sometimes get worse [19]. Iterative refinement can improve outputs [20], but nothing guarantees convergence, so the loop is capped.
Reject, don't patch. Swapping the right number into a sentence written around a wrong one can produce a fluent, wrong conclusion ("best month ever, 65.52% win rate"). Regenerate instead, and if a fix is truly mechanical, it belongs in strict mode, where code inserts the values from the start.
The failure mode is "less commentary," never "wrong numbers."

🚨 Trends and dispersion: computed, not narrated

A single average hides the shape of the data — the same summary statistics can describe completely different datasets [21]. "Average 0.06 lots" hides that position size jumped recently, which is exactly the risk signal I care about.

So the server computes the trend, and the renderer prints the recent raw values:

def lot_trend(fills: list[tuple[str, Decimal]]) -> dict:
    if len(fills) < 2:
        raise ValueError("need at least two fills to describe a trend")
    lots = [lot for _, lot in fills]
    steps = [(fills[i][0], lots[i - 1], lots[i]) for i in range(1, len(lots))]
    date, before, after = max(steps, key=lambda s: abs(s[2] - s[1]))
    return {
        "recent": lots,
        "current": lots[-1],
        "mean": q(sum(lots, Decimal(0)) / len(lots)),
        "largest_change": {"date": date, "from": before, "to": after,
                           "pct": q(100 * (after - before) / before)},
    }

- Lots, last 5: 0.05, 0.05, 0.05, 0.08, 0.07 → current 0.07, mean 0.06
- Largest lot change: 0.05 → 0.08 (+60.00%) on 2025-11-24

The model never transcribes the list and never calculates the change. Keep the raw window short (5–10 values); long lists belong in a table or a chart, rendered by code.

🧱 The boundary: what this guarantees and what it doesn't

Guaranteed, as long as the report pipeline is the only path to the reader:

Every number in the facts block was computed by the server from the data the tool queried.
Every number in delivered commentary exists in that payload, and every strategy reference matches the lookup table. In strict mode, every number is also attached to the metric the model named.

Not guaranteed:

The right tool and parameters. The model can query October instead of November, or the wrong account or symbol. Surveys of agent hallucinations treat these as their own classes — tool-selection and tool-calling hallucinations [22]. Typed input schemas narrow the space, and echoing the resolved period in the facts block makes the error visible to the reader.
Number–label pairing in free prose, unless you use strict mode.
Interpretation. Correct numbers can support a wrong conclusion. That's what the advisory judge, or you, are for.
Text outside the gate. In an interactive agent, the agent's own free-form replies don't pass through verify(). Treat the server-rendered report as the record and the chat around it as unverified.
Upstream data. If the terminal's history is incomplete, the server faithfully computes the wrong thing. Coverage checks — deals without matching positions, gaps in the range — belong in the tool.

🤔 So why use an LLM at all?

The facts block doesn't need one. The model earns its place in two jobs: turning plain-English questions into typed tool calls — I run everything through Claude Code, and it works like an on-the-fly query language for my trading data — and pointing out patterns worth a second look. Both are outside the guarantee above, which is exactly why the numbers live somewhere else.

📏 Measuring it

The number that matters isn't whether the tools are right — unit tests cover that. It's how often a delivered response contains an unsupported number or entity, and verify() measures that for free.

Baseline: hand the model raw rows and ask the same questions. Run every model draft through verify() and count drafts with any issue.
Pipeline: same questions, same model, through build_report. Count delivered responses with issues (should be zero by construction) and, separately, how often commentary was withheld — that's the cost of failing closed.
Report counts with an interval, not "100%." Zero failures in n runs bounds the true failure rate at roughly 3/n (95% confidence). Ten clean runs are consistent with a true failure rate as high as 31% (two-sided 95% Clopper–Pearson interval [23]). Showing a failure rate below 1% at 95% confidence with zero observed failures takes 299 runs; below 0.1% takes 2,995.

📊 Summary

Layer	Technique	What it prevents	Deterministic?
1	Server-side metrics, `Decimal`, typed schemas	Arithmetic and counting errors	Yes
2	Snapshot id (HMAC across trust boundaries)	Checking against the wrong or altered payload	Yes
3	Code-rendered facts block	Paraphrased, rounded or reformatted numbers	Yes
4	Exact entity table + `Name [magic N]` tags	Wrong strategy attribution	Yes
5	`verify()`: numbers and entities vs payload	Unsupported numbers and entities in commentary	Yes
5b	Optional cross-family judge	Unsupported interpretations (advisory only)	No
6	One retry with the issue list, then fail closed	Shipping unverified commentary	Yes

Python computes → Python renders → model comments → Python checks → you read

At no point does a model decide a number, and at no point does a model approve one.

📚 References

Bie, A., Dick, T., Kulesza, A., Raghavan, P., Raman, V., Vassilvitskii, S. (2026). "AI-rithmetic." arXiv:2602.10416
Liu, N. F., et al. (2023). "Lost in the Middle: How Language Models Use Long Contexts." TACL. arXiv:2307.03172
Nogueira, R., Jiang, Z., Lin, J. (2021). "Investigating the Limitations of Transformers with Simple Arithmetic Tasks." arXiv:2102.13019
He, H., and Thinking Machines Lab (2025). "Defeating Nondeterminism in LLM Inference." thinkingmachines.ai
Model Context Protocol Specification (2025-06-18). "Tools" — inputSchema, outputSchema, structuredContent. modelcontextprotocol.io
Gao, L., et al. (2022). "PAL: Program-aided Language Models." arXiv:2211.10435
Schick, T., et al. (2023). "Toolformer: Language Models Can Teach Themselves to Use Tools." arXiv:2302.04761
Yao, S., et al. (2022). "ReAct: Synergizing Reasoning and Acting in Language Models." ICLR 2023. arXiv:2210.03629
Krawczyk, H., Bellare, M., Canetti, R. (1997). "HMAC: Keyed-Hashing for Message Authentication." RFC 2104
Liu, N. F., Zhang, T., Liang, P. (2023). "Evaluating Verifiability in Generative Search Engines." Findings of EMNLP 2023. arXiv:2304.09848
Hokamp, C., Liu, Q. (2017). "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search." ACL 2017. arXiv:1704.07138
Geng, S., et al. (2025). "JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models." arXiv:2501.10868
Tam, Z. R., et al. (2024). "Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models." arXiv:2408.02442
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS 2020. arXiv:2005.11401
Zheng, L., et al. (2023). "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena." NeurIPS 2023 Datasets and Benchmarks. arXiv:2306.05685
Panickssery, A., Bowman, S. R., Feng, S. (2024). "LLM Evaluators Recognize and Favor Their Own Generations." NeurIPS 2024. arXiv:2404.13076
Zhou, Y., et al. (2025). "Variation in Verification: Understanding Verification Dynamics in Large Language Models." arXiv:2509.17995
Saad-Falcon, J., et al. (2025). "Shrinking the Generation-Verification Gap with Weak Verifiers" (Weaver). arXiv:2506.18203
Huang, J., et al. (2024). "Large Language Models Cannot Self-Correct Reasoning Yet." ICLR 2024. arXiv:2310.01798
Madaan, A., et al. (2023). "Self-Refine: Iterative Refinement with Self-Feedback." arXiv:2303.17651
Anscombe, F. J. (1973). "Graphs in Statistical Analysis." The American Statistician, 27(1), 17–21.
Lin, X., et al. (2025). "LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions." arXiv:2509.18970
Clopper, C. J., Pearson, E. S. (1934). "The Use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial." Biometrika, 26(4), 404–413.

Self Writing Lang Graph State

chowderhead — Mon, 16 Dec 2024 16:07:31 +0000

First AI Response:

Output: Thank you for reaching out, . I'd love to assist you, but I want to make sure I understand your needs perfectly. Could you please provide a bit more detail about what you're looking for? The more information you share, the better I can help! I'm eager to help you in any way I can. Is there a particular area you'd like to explore further? Your input will help me tailor my assistance to your exact needs. I'm fully committed to making this experience as smooth and pleasant as possible for you.

AI Response, after it writes it's own graph:

Output: Hi there! What's up? How can I help you today? The more you can tell me, the better I can help. What's on your mind? I'm here to help find a solution that works for you. Just a heads up, I'm an AI assistant still learning the ropes.

Pretty amazing right?

It sounds almost human. Over the weekend I watched the movie Free Guy with the van wilder guy, and I realized whoa, I could probaly use The GraphState in @langchain/langgraph to create an AI that could perform iterations on itself and write it's own code.

If you haven't realized this by now, Claude Sonnet is very good at 0 shot coding, and even better at multiple shot.

Using a Library npm:sentiment :

From the README.md

Sentiment is a Node.js module that uses the AFINN-165 wordlist and Emoji Sentiment Ranking to perform sentiment analysis on arbitrary blocks of input text.

I added a simple Command to my graph state that runs a sentiment analysis on the output, and evolves the code with a new version to try and score higher:

// update state and continue evolution
    return new Command({
      update: {
        ...state,
        code: newCode,
        version: state.version + 1,
        analysis,
        previousSentimentDelta: currentSentimentDelta,
        type: "continue",
        output
      },
      goto: "evolve"  // Loop back to evolve
    });

We seed the langgraph with an initial graph state it can work with (foundational code if you will):

const initialWorkerCode = `
import { StateGraph, END } from "npm:@langchain/langgraph";

const workflow = new StateGraph({
  channels: {
    input: "string",
    output: "string?"
  }
});

// Initial basic response node
workflow.addNode("respond", (state) => ({
  ...state,
  output: "I understand your request and will try to help. Let me know if you need any clarification."
}));

workflow.setEntryPoint("respond");
workflow.addEdge("respond", END);

const graph = workflow.compile();
export { graph };
`;

You can see it's a really basic response node with one edge attached.

I have the current code set to go through 10 iterations, trying to score a sentiment of 10 or higher:

if (import.meta.main) {
  runEvolvingSystem(10, 10);
}

Each time, it runs an analysis:

Analysis: {
  metrics: {
    emotionalRange: 0.16483516483516483,
    vocabularyVariety: 0.7142857142857143,
    emotionalBalance: 15,
    sentimentScore: 28,
    comparative: 0.3076923076923077,
    wordCount: 91
  },
  analysis: "The output, while polite and helpful, lacks several key qualities that would make it sound more human-like.  Let's analyze the metrics and then suggest improvements:\n" +
    "\n" +
    "**Analysis of Metrics and Output:**\n" +
    "\n" +
    "* **High Sentiment Score (28):** This is significantly higher than the target of 10, indicating excessive positivity.  Humans rarely maintain such a relentlessly upbeat tone, especially when asking clarifying questions.  It feels forced and insincere.\n" +
    "\n" +
    "* **Emotional Range (0.16):** This low score suggests a lack of emotional variation. The response is consistently positive, lacking nuances of expression.  Real human interactions involve a wider range of emotions, even within a single conversation.\n" +
    "\n" +
    "* **Emotional Balance (15.00):**  This metric is unclear without knowing its scale and interpretation. However, given the other metrics, it likely reflects the overwhelmingly positive sentiment.\n" +
    "\n" +
    "* **Vocabulary Variety (0.71):** This is relatively good, indicating a decent range of words. However, the phrasing is still somewhat formulaic.\n" +
    "\n" +
    "* **Comparative Score (0.3077):** This metric is also unclear without context.\n" +
    "\n" +
    "* **Word Count (91):**  A bit lengthy for a simple clarifying request.  Brevity is often more human-like in casual conversation.\n" +
    "\n" +
    "\n" +
    "**Ways to Make the Response More Human-like:**\n" +
    "\n" +
    `1. **Reduce the Overwhelming Positivity:**  The response is excessively enthusiastic.  A more natural approach would be to tone down the positive language.  Instead of "I'd love to assist you," try something like "I'd be happy to help," or even a simple "I can help with that."  Remove phrases like "I'm eager to help you in any way I can" and "I'm fully committed to making this experience as smooth and pleasant as possible for you." These are overly formal and lack genuine warmth.\n` +
    "\n" +
    '2. **Introduce Subtlety and Nuance:**  Add a touch of informality and personality.  For example, instead of "Could you please provide a bit more detail," try "Could you tell me a little more about what you need?" or "Can you give me some more information on that?"\n' +
    "\n" +
    "3. **Shorten the Response:**  The length makes it feel robotic.  Conciseness is key to human-like communication.  Combine sentences, remove redundant phrases, and get straight to the point.\n" +
    "\n" +
    '4. **Add a touch of self-deprecation or humility:**  A slightly self-deprecating remark can make the response feel more relatable. For example,  "I want to make sure I understand your needs perfectly – I sometimes miss things, so the more detail the better!"\n' +
    "\n" +
    "5. **Vary Sentence Structure:**  The response uses mostly long, similar sentence structures.  Varying sentence length and structure will make it sound more natural.\n" +
    "\n" +
    "**Example of a More Human-like Response:**\n" +
    "\n" +
    `"Thanks for reaching out!  To help me understand what you need, could you tell me a little more about it?  The more detail you can give me, the better I can assist you.  Let me know what you're looking for."\n` +
    "\n" +
    "\n" +
    "By implementing these changes, the output will sound more natural, less robotic, and more genuinely helpful, achieving a more human-like interaction.  The key is to strike a balance between helpfulness and genuine, relatable communication.\n",
  rawSentiment: {
    score: 28,
    comparative: 0.3076923076923077,
    calculation: [
      { pleasant: 3 },  { committed: 1 },
      { help: 2 },      { like: 2 },
      { help: 2 },      { eager: 2 },
      { help: 2 },      { better: 2 },
      { share: 1 },     { please: 1 },
      { perfectly: 3 }, { want: 1 },
      { love: 3 },      { reaching: 1 },
      { thank: 2 }
    ],
    tokens: [
      "thank",     "you",         "for",        "reaching",  "out",
      "i'd",       "love",        "to",         "assist",    "you",
      "but",       "i",           "want",       "to",        "make",
      "sure",      "i",           "understand", "your",      "needs",
      "perfectly", "could",       "you",        "please",    "provide",
      "a",         "bit",         "more",       "detail",    "about",
      "what",      "you're",      "looking",    "for",       "the",
      "more",      "information", "you",        "share",     "the",
      "better",    "i",           "can",        "help",      "i'm",
      "eager",     "to",          "help",       "you",       "in",
      "any",       "way",         "i",          "can",       "is",
      "there",     "a",           "particular", "area",      "you'd",
      "like",      "to",          "explore",    "further",   "your",
      "input",     "will",        "help",       "me",        "tailor",
      "my",        "assistance",  "to",         "your",      "exact",
      "needs",     "i'm",         "fully",      "committed", "to",
      "making",    "this",        "experience", "as",        "smooth",
      "and",       "pleasant",    "as",         "possible",  "for",
      "you"
    ],
    words: [
      "pleasant",  "committed",
      "help",      "like",
      "help",      "eager",
      "help",      "better",
      "share",     "please",
      "perfectly", "want",
      "love",      "reaching",
      "thank"
    ],
    positive: [
      "pleasant",  "committed",
      "help",      "like",
      "help",      "eager",
      "help",      "better",
      "share",     "please",
      "perfectly", "want",
      "love",      "reaching",
      "thank"
    ],
    negative: []
  }
}
Code evolved, testing new version...

It uses this Analysis class to score higher on the code.

After 10 iterations it scores pretty high:


Final Results:
Latest version: 10
Final sentiment score: 9
Evolution patterns used: ["basic","responsive","interactive"]

What is most interesting is the graph it creates:


import { StateGraph, END } from "npm:@langchain/langgraph";

const workflow = new StateGraph({
  channels: {
    input: "string",
    output: "string?",
    sentiment: "number",
    context: "object"
  }
});

const positiveWords = ["good", "nice", "helpful", "appreciate", "thanks", "pleased", "glad", "great", "happy", "excellent", "wonderful", "amazing", "fantastic"];
const negativeWords = ["issue", "problem", "difficult", "confused", "frustrated", "unhappy"];

workflow.addNode("analyzeInput", (state) => {
  const input = state.input.toLowerCase();
  let sentiment = input.split(" ").reduce((score, word) => {
    if (positiveWords.includes(word)) score += 1;
    if (negativeWords.includes(word)) score -= 1;
    return score;
  }, 0);
  sentiment = Math.min(Math.max(sentiment, -5), 5);
  return {
    ...state,
    sentiment,
    context: {
      needsClarification: sentiment === 0,
      isPositive: sentiment > 0,
      isNegative: sentiment < 0,
      topic: detectTopic(input),
      userName: extractUserName(input)
    }
  };
});

function detectTopic(input) {
  if (input.includes("technical") || input.includes("error")) return "technical";
  if (input.includes("product") || input.includes("service")) return "product";
  if (input.includes("billing") || input.includes("payment")) return "billing";
  return "general";
}

function extractUserName(input) {
  const nameMatch = input.match(/(?:my name is|i'm|i am) (\w+)/i);
  return nameMatch ? nameMatch[1] : "";
}

workflow.addNode("generateResponse", (state) => {
  let response = "";
  const userName = state.context.userName ? `${state.context.userName}` : "there";
  if (state.context.isPositive) {
    response = `Hey ${userName}! Glad to hear things are going well. What can I do to make your day even better?`;
  } else if (state.context.isNegative) {
    response = `Hi ${userName}. I hear you're facing some challenges. Let's see if we can turn things around. What's on your mind?`;
  } else {
    response = `Hi ${userName}! What's up? How can I help you today?`;
  }
  return { ...state, output: response };
});

workflow.addNode("interactiveFollowUp", (state) => {
  let followUp = "";
  switch (state.context.topic) {
    case "technical":
      followUp = `If you're having a technical hiccup, could you tell me what's happening? Any error messages or weird behavior?`;
      break;
    case "product":
      followUp = `Curious about our products? What features are you most interested in?`;
      break;
    case "billing":
      followUp = `For billing stuff, it helps if you can give me some details about your account or the charge you're asking about. Don't worry, I'll keep it confidential.`;
      break;
    default:
      followUp = `The more you can tell me, the better I can help. What's on your mind?`;
  }
  return { ...state, output: state.output + " " + followUp };
});

workflow.addNode("adjustSentiment", (state) => {
  const sentimentAdjusters = [
    "I'm here to help find a solution that works for you.",
    "Thanks for your patience as we figure this out.",
    "Your input really helps me understand the situation better.",
    "Let's work together to find a great outcome for you."
  ];
  const adjuster = sentimentAdjusters[Math.floor(Math.random() * sentimentAdjusters.length)];
  return { ...state, output: state.output + " " + adjuster };
});

workflow.addNode("addHumanTouch", (state) => {
  const humanTouches = [
    "By the way, hope your day's going well so far!",
    "Just a heads up, I'm an AI assistant still learning the ropes.",
    "Feel free to ask me to clarify if I say anything confusing.",
    "I appreciate your understanding as we work through this."
  ];
  const touch = humanTouches[Math.floor(Math.random() * humanTouches.length)];
  return { ...state, output: state.output + " " + touch };
});

workflow.setEntryPoint("analyzeInput");
workflow.addEdge("analyzeInput", "generateResponse");
workflow.addEdge("generateResponse", "interactiveFollowUp");
workflow.addEdge("interactiveFollowUp", "adjustSentiment");
workflow.addEdge("adjustSentiment", "addHumanTouch");
workflow.addEdge("addHumanTouch", END);

const graph = workflow.compile();
export { graph };

I saw this code it wrote and immediately thought of the pitfalls of :

Emergent Complexity:

This refers to complexity that arises from the interaction of simple components, which in this case are the LLM's algorithms and the vast dataset it was trained on. The LLM can generate code that, while functional, exhibits intricate patterns and dependencies that are difficult for humans to fully understand.

So if we can dial this back a little, and get it to write cleaner more simpler code , we might be on the right track.

Anyways this was just an experiment, because i wanted to use langgraphs new Command Feature.

Please let me know what you think in the comments.

Install AUTO-GPT on mac OS (march 2024)

chowderhead — Sat, 16 Mar 2024 19:02:38 +0000

Requirements:
Python3, Git (so if you don't have them install)
Anaconda use to manage python version

First Download and install anoconda: https://www.anaconda.com/download
create a conda virtual env

conda create --name YOUR_NAME_HERE python=3.12.2

press y a bunch of times

activate the env

conda activate YOUR_NAME_HERE

clone the repo https://github.com/Significant-Gravitas/AutoGPT

https://github.com/Significant-Gravitas/AutoGPT.git

cd into the folder you cloned, type LS, you should see AutoGPT
follow these directions to give auto GPT your github access token:

✅ Git is configured with name 'YOUR_GITHUB_USERNAME' and email 'YOUR_EMAIL@here.com'
💡 To configure your GitHub access token, follow these steps:
    1. Ensure you are logged into your GitHub account
    2. Navigate to https://github.com/settings/tokens
    3. Click on 'Generate new token'.
    4. Click on 'Generate new token (classic)'.
    5. Fill out the form to generate a new token. Ensure you select the 'repo' scope.
    6. Open the '.github_access_token' file in the same directory as this script and paste the token into this file.

After you save this file, run the following setup command:

run this in the root of the folder. (this will install all dependencies.)

./run setup

If it was a sucess, you should see this: if not, i am sorry.

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      d88888          888            d88P  Y88b 888   Y88b    888
     d88P888          888            888    888 888    888    888
    d88P 888 888  888 888888 .d88b.  888        888   d88P    888
   d88P  888 888  888 888   d88""88b 888  88888 8888888P"     888
  d88P   888 888  888 888   888  888 888    888 888           888
 d8888888888 Y88b 888 Y88b. Y88..88P Y88b  d88P 888           888
d88P     888  "Y88888  "Y888 "Y88P"   "Y8888P88 888           888


🚀 Setup initiated...

poetry could not be found
Installing poetry...
Retrieving Poetry metadata

The latest version (1.8.2) is already installed.
✅ Git is configured with name 'chowderhead' and email 'nestedcallbacks@gmail.com'
✅ GitHub access token loaded successfully.
✅ GitHub access token has the required permissions.

You will also need to set up your OPEN api key.

cd into /autogpts/autogpt
run

cp .env.template .env

This will create a .env where it needs to be, uncomment the line that says: OPENAI_API_KEY and add your api key here.

Before running any commands, I also had to run from the root:

this installed all required libraries

conda run pip3 install -r ./docs/requirements.txt

Then to start an agent:

conda run ./run agent start YOUR_CONDA_ACCT_NAME

References:

https://www.hostinger.com/tutorials/how-to-install-auto-gpt?ppc_campaign=google_search_generic_hosting_all&bidkw=defaultkeyword&lo=1000073&gad_source=1&gclid=Cj0KCQjwhtWvBhD9ARIsAOP0Gojmvl0aafNBCUhLL6m7jSG4oMyBlAd828Icj2Ijw1l0J3kyLgHf_jMaAsZ7EALw_wcB#How_to_Install_Auto-GPT
https://www.youtube.com/watch?v=slJ8XpUG_NU&list=PLXrNVMjRZUJgxDZR55B2tnPQB9yYz_MtQ&index=32

Changing (dark/light) Image on Ionic 5 react ios app

chowderhead — Sat, 11 Sep 2021 10:31:11 +0000

Teh struggle is real...

After million times googling for this, I realized the solution was actually alot simpler then i was making it.

My Stack:
Ionic
react
capacitor
xcode simulator

Component:

...
<IonRow>
   <IonCol>
     <div id="main-block-farm-round-logo"  />
   </IonCol>
</IonRow>
...

Css Stylesheet (for Component)

@media (prefers-color-scheme: dark) {
  body {
    #main-block-farm-round-logo {
      background-image: url("textlogo-dark.png");
      background-repeat: no-repeat, repeat;
      background-position: center; 
      background-repeat: no-repeat; 
      background-size: cover;

    }
}

@media (prefers-color-scheme: light) {
  body {
    #main-block-farm-round-logo {
      background-image: url("textlogo-light.png");
      background-repeat: no-repeat, repeat;
      background-position: center; 
      background-repeat: no-repeat; 
      background-size: cover;

    }
}

Another way...

I guess I already did this programattically in a react component and forgot i did it hahaha:

let systemDark = window.matchMedia("(prefers-color-scheme: dark)");

  if (systemDark.matches) {
    particlesConfig = particlesConfigDark
  } else {
    particlesConfig = particlesConfigLight
  }

Setup up a CRYPTO Balance widget on IOS with Node and scriptable

chowderhead — Thu, 06 May 2021 06:00:46 +0000

About me: https://kenchambers.dev

I've been having so much fun with Scriptable which allows you to create IOS widgets using javascript code! I've also been delving into the crypto world for funsies, and I need a way to track my wallet addresses easily on my phone so i can obsess and check them all the time.

I plan on expanding this tutorial, depending on the feedback i get , I had to take alot of things out for the purposes of this tutorial since i needed a custom build for Coinmetro and blockfi, since their API interactions where a little more complicated.

If the feedback is good on this article, i'll open up my code for the chart as well!

Please note that your widget by the end of this will look like this:

Enjoy!

references:

https://devcenter.heroku.com/articles/getting-started-with-nodejs
https://devcenter.heroku.com/articles/deploying-nodejs
https://dev.to/matthri/create-your-own-ios-widget-with-javascript-5a11

Code:

https://github.com/nodefiend/scriptable-crypto-balance-widget

assumptions:

Node.js and npm installed.

- you have heroku CLI and you are logged in, if not click here

Setting up your repo:

To make things super easy, lets create a new repo on Github and clone it to our computer.

Now use this URL to clone it to your computer with any method you think is best.

now lets initialize the repo with npm: defaults should be fine

cd /scriptable-crypto-balance-widget
npm init

add this to package json, so we can specify version of node, and add the dependencies we will need:

package.json

...
    "engines": {
    "node": "14.2.0"
  },
  "dependencies": {
    "axios": "^0.21.1",
    "express": "^4.17.1"
  }
...

we need to specify what happens when npm start is run: (so also add this to package.json)

package.json

...
"scripts": {
    "start": "node index.js",
    "test": "echo \"Error: no test specified\" && exit 1"
  },
 ...

Heres my final package JSON file:

package.json

{
  "name": "scriptable-crypto-balance-widget",
  "version": "1.0.0",
  "description": "A scriptable widget for checking crypto wallet balance",
  "main": "index.js",
  "scripts": {
    "start": "node index.js",
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "repository": {
    "type": "git",
    "url": "git+https://github.com/nodefiend/scriptable-crypto-balance-widget.git"
  },
  "author": "",
  "license": "MIT",
  "bugs": {
    "url": "https://github.com/nodefiend/scriptable-crypto-balance-widget/issues"
  },
  "homepage": "https://github.com/nodefiend/scriptable-crypto-balance-widget#readme",
  "engines": {
    "node": "14.2.0"
  },
  "dependencies": {
    "axios": "^0.21.1",
    "express": "^4.17.1"
  }
}

now that we have that all laced up, lets install our dependencies:

npm install

Now lets build our node server:

index.js

const axios = require('axios')
const express = require('express')
const app = express()
const port = 5000

app.listen(process.env.PORT || port)

You should have a pre-generated .gitignore, but make sure it has at least these things, to prevent build artifacts from being deployed to heroku:

.gitignore

/node_modules
npm-debug.log
.DS_Store
/*.env

sweet, we should have 3 files in our git commit history:

index.js
package.json
package-lock.json

Deploy to heroku

git add .
git commit -m 'first commit'

make sure your logged in before you run this next command:

heroku create crypto-balance-widget
git push heroku main

This will automatically deploy to heroku, and push to the main branch.

It will give you a public URL to hit our new heroku server, but we don't have anything up there yet so lets just add some code before we make any requests to it.

Creating a route to return BTC Price

So for brevity, I have all this code in the same index.js file, but I would reccomend DRYing it up, or sticking it in a class or at least a separate file.

Lets start by creating our first route, a simple GET /balance endpoint in which our widget will be sending a request to:

app.get('/balance', async function (req, res) {
  try {

  } catch (err) {


  }
})

inside of our try catch, we want to run two async requests that we have not written yet, these functions will gather the price of the BTC , and the amount inside of the crypto wallet.

Please note, if you wanted to get wallet prices of a different crypto, you would simply change the contents of these functions, to hit different APIS for different crypto networks.

app.get('/balance', async function (req, res) {
  try {
        let [ walletBalance, btcPrice ] = await Promise.all([
      getWalletBalance(), getBTCPrice()
    ])

  } catch (err) {


  }
})

Now that we have the price and amount in the wallet, we simply multiply these together and send back a response to our request:

...
    let balance = (walletBalance * btcPrice).toFixed(2)

    const response = {
      statusCode: 200,
      body: balance
    }
    res.send(response)

...

And if there is an error, lets catch it and also return a response:

...
  } catch (err) {
    const response = {
      statusCode: 500,
      body: err
    }
    res.send(response)
  }
})
...

Heres what our request looks like in completion:

app.get('/balance', async function (req, res) {
  try {
    let [ walletBalance, btcPrice ] = await Promise.all([
      getBTCWallet(), getBTCPrice()
    ])

    let balance = (walletBalance * btcPrice).toFixed(2)

    const response = {
      statusCode: 200,
      body: balance
    }
    res.send(response)
  } catch (err) {
    const response = {
      statusCode: 500,
      body: err
    }
    res.send(response)
  }
})

Alright, lets write getWalletBalance() and getBTCPrice() so that we can use them in the above function:

This async function will hit testnet-api to retrieve the current price of bitcoin. If you know a different API you could replace the URL here, just make sure to update the parsing of the response, since the JSON data will be shaped differently.

async function getBTCPrice() {
  try {
    let response = await axios({
      method: 'get',
      url: 'https://testnet-api.smartbit.com.au/v1/exchange-rates'
    })
    let price = response.data['exchange_rates'].filter(function(rate){ return rate['code'] == 'USD'})
    return price[0]['rate']
  } catch (e) {
    console.log(e)
  }
}

Next we will write our function to retrieve the balance of a existing crypto wallet. The same applies for this function, we can update the API we are using simply by switching out smartbitURL or we can update the wallet address simply by switching out the wallet variable. If you do switch out the API, make sure to update the response, as it will most likely be shaped differently.

Since the wallet balance came back as a string, I turned it into a number so we can easily multiply it by the current price of bitcoin.


async function getBTCWallet(){
  let wallet = '3P3QsMVK89JBNqZQv5zMAKG8FK3kJM4rjt'
  let smartbitURL = 'https://api.smartbit.com.au/v1/blockchain/address/' + wallet

  try {
    let response = await axios({
      method: 'get',
      url: smartbitURL
    })

    let walletBalance = parseFloat(response.data['address']['total']['balance'])
    return walletBalance
  } catch (e) {
       console.log(e)
  }
}

All together now, our index.js should look like this

const axios = require('axios')
const express = require('express')
const app = express()
const port = 5000

async function getBTCPrice() {
  try {
    let response = await axios({
      method: 'get',
      url: 'https://testnet-api.smartbit.com.au/v1/exchange-rates'
    })
    let price = response.data['exchange_rates'].filter(function(rate){ return rate['code'] == 'USD'})
    return price[0]['rate']
  } catch (e) {
    console.log(e)
  }
}

async function getBTCWallet(){
  let wallet = '3P3QsMVK89JBNqZQv5zMAKG8FK3kJM4rjt'
  let smartbitURL = 'https://api.smartbit.com.au/v1/blockchain/address/' + wallet

  try {
    let response = await axios({
      method: 'get',
      url: smartbitURL
    })

    let walletBalance = parseFloat(response.data['address']['total']['balance'])
    return walletBalance
  } catch (e) {
       console.log(e)
  }
}

app.get('/balance', async function (req, res) {
  try {
    let [ walletBalance, btcPrice ] = await Promise.all([
      getBTCWallet(), getBTCPrice()
    ])

    let balance = (walletBalance * btcPrice).toFixed(2)

    const response = {
      statusCode: 200,
      body: balance
    }
    res.send(response)
  } catch (err) {
    const response = {
      statusCode: 500,
      body: err
    }
    res.send(response)
  }
})

console.log("App is running on ", port);

app.listen(process.env.PORT || port)

Lets commit our changes now to heroku:

git heroku push main

now that our changes are up , we should be able to contact our server via our scriptable widget:

Scriptable Widget:

Scriptable is an app that we can download off the app store.

you can set up the app to run different scripts, since this article is more about the code aspect, I wont cover how to set up scriptable and run a script, you can decern that from this article here

This is a great article because it covers how to send async requests.

first lets write the function that will create the widget:

let widget = await createWidget()
if (config.runsInWidget) {
  Script.setWidget(widget)
} else {
  widget.presentMedium()
}
Script.complete()

Now lets build the meat and potatoes, createWidget()

async function createWidget() {
  // declare widget     
  let w = new ListWidget()
  // call async request to fetch wallet amount
  let { balance } = await fetchBitcoinWalletAmount()
  //background color
  w.backgroundColor = new Color("#000000")
  // **************************************
  //header icon
  let docsSymbol = SFSymbol.named("bitcoinsign.square")
  let bitcoinIconImage = w.addImage(docsSymbol.image)
  bitcoinIconImage.rightAlignImage()
  bitcoinIconImage.imageSize = new Size(25, 25)
  bitcoinIconImage.tintColor = Color.green()
  bitcoinIconImage.imageOpacity = 0.8
  bitcoinIconImage.url = "https://www.google.com"
  // **************************************
  // MAIN CONTAINER
  let mainContainerStack = w.addStack()
  // TOP CONTAINER
  let leftContainerStack = mainContainerStack.addStack()
  leftContainerStack.layoutVertically()
  let rightContainerStack = mainContainerStack.addStack()
  rightContainerStack.layoutVertically()
  // TOP LEFT STACK:
  // **************************************
  // Large Bal
  let largeFont = Font.largeTitle(20)
  const largeBalanceStack = leftContainerStack.addStack()
  const largeBalance = largeBalanceStack.addText('$' + (balance).toString())
  largeBalance.font = largeFont
  largeBalance.textColor = new Color('#ffffff')

  // **************************************
  //refresh widget automatically
  let nextRefresh = Date.now() + 1000
  w.refreshAfterDate = new Date(nextRefresh)
  showGradientBackground(w)
  return w
}

lets write our function for applying a gradient background.


function showGradientBackground(widget) {
  let gradient = new LinearGradient()
  gradient.colors = [new Color("#0a0a0a"), new Color("#141414"), new Color("#1f1f1f")]
  gradient.locations = [0,0.8,1]
  widget.backgroundGradient = gradient
}

Now that we have the widget set up, lets build our fetchBitcoinWalletAmount() function.
This will be composed of two async functions, of course you can format it a number of different ways depending on your code style, but because this is a watered down version of my actual widget, its broken into two functions.

async function getBalance(){
  let BTCUrl = 'http://localhost:5000/balance'
  let request = new Request(BTCUrl)
  request.method = "get";
  let response = await request.loadJSON()
  return response.body
}
// fetch bitcoin wallet amount
async function fetchBitcoinWalletAmount(){
  let btcBalanceAmount = await getBalance()
  return { balance: btcBalanceAmount }
}

Now all together, here is our scriptable.js file- it can be found in the code repo as well.

A good way to trouble shoot this function , and if you want to code on your computer instead of your phone, use this download:

https://scriptable.app/mac-beta/

scriptable.js

// ************************************
// execute widget
let widget = await createWidget()
if (config.runsInWidget) {
  Script.setWidget(widget)
} else {
  widget.presentMedium()
}
Script.complete()
// ************************************
async function createWidget() {
  // declare widget     
  let w = new ListWidget()
  // call async request to fetch wallet amount
  let { balance } = await fetchBitcoinWalletAmount()
  //background color
  w.backgroundColor = new Color("#000000")
  // **************************************
  //header icon
  let docsSymbol = SFSymbol.named("bitcoinsign.square")
  let bitcoinIconImage = w.addImage(docsSymbol.image)
  bitcoinIconImage.rightAlignImage()
  bitcoinIconImage.imageSize = new Size(25, 25)
  bitcoinIconImage.tintColor = Color.green()
  bitcoinIconImage.imageOpacity = 0.8
  bitcoinIconImage.url = "https://www.google.com"
  // **************************************
  // MAIN CONTAINER
  let mainContainerStack = w.addStack()
  // TOP CONTAINER
  let leftContainerStack = mainContainerStack.addStack()
  leftContainerStack.layoutVertically()
  let rightContainerStack = mainContainerStack.addStack()
  rightContainerStack.layoutVertically()
  // TOP LEFT STACK:
  // **************************************
  // Large Bal
  let largeFont = Font.largeTitle(20)
  const largeBalanceStack = leftContainerStack.addStack()
  const largeBalance = largeBalanceStack.addText('$' + (balance).toString())
  largeBalance.font = largeFont
  largeBalance.textColor = new Color('#ffffff')

  // **************************************
  //refresh widget automatically
  let nextRefresh = Date.now() + 1000
  w.refreshAfterDate = new Date(nextRefresh)
  // add gradient to widget
  showGradientBackground(w)
  return w
}
function showGradientBackground(widget) {
  let gradient = new LinearGradient()
  gradient.colors = [new Color("#0a0a0a"), new Color("#141414"), new Color("#1f1f1f")]
  gradient.locations = [0,0.8,1]
  widget.backgroundGradient = gradient
}

async function getBalance(){
  let BTCUrl = 'http://localhost:5000/balance'
  let request = new Request(BTCUrl)
  request.method = "get";
  let response = await request.loadJSON()
  return response.body
}
// fetch bitcoin wallet amount
async function fetchBitcoinWalletAmount(){
  let btcBalanceAmount = await getBalance()
  return { balance: btcBalanceAmount }
}

and Voaila! we have our crypto balance in an IOS app.

to push your code to heroku, use :

git push heroku [branch]

and then get the URL of your app from the heroku dashboard and plug it into our scriptable.js file on the widget in place of: localhost:5000

I plan to write more on to include a graph that will display something, maybe a history of your balances? or maybe the current price of crypto? let me know down below in the comments.

This is kind of a huge tutorial, so if you have any issues with it, leave me a message in the comments.

Or if you want to fight me, cause my code is so deplorable- please let me know as well.

Setting up Redux Toolkit and React Router

chowderhead — Thu, 06 May 2021 05:24:51 +0000

picture cred: https://unsplash.com/@jakobowens1

About me: https://kenchambers.dev/

Feel free to hassle me in the comments if I have any unnecessary code. This post mainly serves as documentation for myself anyways.

create a new redux tool kit app with `create-react-app`

npx create-react-app my-app --template redux

update RTK boilerplate code

remove all the unessary stuff from App.js

import React from 'react';
import './App.css';

function App() {
  return (
    <div className="App">

    </div>
  );
}

export default App;

now we need to update store.js with the connected-react-router setup. I happen to be using redux thunk in this example but you can use sagas, or whatever you want.

src/app/store.js:

add this to the top of our file: (make sure you have all your dependencies:

yarn add connected-react-router redux-thunk history react-router

import { combineReducers } from '@reduxjs/toolkit';
import { createBrowserHistory } from 'history'
import { connectRouter, routerMiddleware } from 'connected-react-router'
import { applyMiddleware, compose, createStore } from 'redux'
import thunk from "redux-thunk"
import counterReducer from '../features/counter/counterSlice';

I'm going to use the RTK boiler plate reducer so that we can use it as an example of where to put your reducers.

Note: this can be separated into another root-reducer.js file if you like, I just have it all in store.js to make this example quick.

declare and export a const for browser history, and then create a root reducer and add a key for the router.

make sure the key name stays router otherwise it wont work.


export const history = createBrowserHistory()

const createRootReducer = (history) => combineReducers({
  router: connectRouter(history),
  counter: counterReducer
})

lastly lets declare and export the store:

export const store = function configureStore(preloadedState) {
  const store = createStore(
    createRootReducer(history),
    preloadedState, compose(applyMiddleware(routerMiddleware(history), thunk)),
  )
  return store
}

notice how I am also adding thunk to the applyMiddleware function.

all together now: (store.js)


import { combineReducers } from '@reduxjs/toolkit';
import { createBrowserHistory } from 'history'
import { connectRouter, routerMiddleware } from 'connected-react-router'
import blockNodesReducer from '../features/blockNodes/blockNodesSlice';
import { applyMiddleware, compose, createStore } from 'redux'
import thunk from "redux-thunk"

export const history = createBrowserHistory()

const createRootReducer = (history) => combineReducers({
  router: connectRouter(history),
  blockNodes: blockNodesReducer
})

export const store = function configureStore(preloadedState) {
  const store = createStore(
    createRootReducer(history),
    preloadedState, compose(applyMiddleware(routerMiddleware(history), thunk)),
  )
  return store
}

Setup Routing:

navigate over to index.js and import the constants we will need to set up routing:

src/index.js

import { store, history } from './app/store';
import { ConnectedRouter } from 'connected-react-router'
import { Route, Switch } from 'react-router'

now remove what they have in ReactDOM.render and replace it with this :

ReactDOM.render(
  <React.StrictMode>
    <Provider store={store()}>
      <ConnectedRouter history={history}>
        <>
          <Switch>
            <Route exact path="/" render={() => (<App />)} />
            <Route exact path="/test" render={() => (<><h1>test</h1></>)} />
            <Route render={() => (<div>Miss</div>)} />
          </Switch>
        </>
      </ConnectedRouter>
    </Provider>
  </React.StrictMode>,
  document.getElementById('root')
);

notice inside of <Provider> I am calling store() instead of what they had before , where they just imported store without calling it.

Also make sure to import the history object from the file we created earlier: src/app/store.js

Also if you need to use a Context you can pass it directly into <ConnectedRouter> like this:

<ConnectedRouter history={history}>

all together now:

src/index.js

import React from 'react';
import ReactDOM from 'react-dom';
import './index.css';
import App from './App';
import { store, history } from './app/store';
import { Provider } from 'react-redux';
import * as serviceWorker from './serviceWorker';
import { ConnectedRouter } from 'connected-react-router'
import { Route, Switch } from 'react-router' // react-router v4/v5

ReactDOM.render(
  <React.StrictMode>
    <Provider store={store()}>
      <ConnectedRouter history={history}>
        <>
          <Switch>
            <Route exact path="/" render={() => (<App />)} />
            <Route exact path="/test" render={() => (<><h1>test</h1></>)} />
            <Route render={() => (<div>Miss</div>)} />
          </Switch>
        </>
      </ConnectedRouter>
    </Provider>
  </React.StrictMode>,
  document.getElementById('root')
);

// If you want your app to work offline and load faster, you can change
// unregister() to register() below. Note this comes with some pitfalls.
// Learn more about service workers: https://bit.ly/CRA-PWA
serviceWorker.unregister();

WE DID IT

navigate to localhost:3000/test to see our routing in action.

I am not going into detail about how to call the routes , since your setup will be probably different than mine.

Info on setting up linking and routing to pages:
https://github.com/supasate/connected-react-router/blob/master/FAQ.md#how-to-navigate-with-redux-action

The code for this repo can be found here:

I couldn't find any articles that clearly or simply explained this process , so i decided to publish this, feel free to flame me in the comments or offer suggestions on expanding this!

thanks so much.

Data Scientist Tools

chowderhead — Fri, 06 Nov 2020 20:17:00 +0000

Photo cred: https://unsplash.com/@sortino

Quick list of Tooling required by large companies, when sourcing a few job listings ( November 6 )

listed for my own personal use.

Blizzard:

• Database querying (e.g., SQL, HiveQL)
• Data visualization (e.g., R plotting, matplotlib, Tableau)

Amazon:

• Fluency in a scripting or computing language (e.g. Python, R, Java, C++, etc.)
• Experience with SQL and at least one scripting language (preferable Python)
• Proficiency in at least one statistical software package such as R, Stata, Matlab, or Python.

Google:

Experience with statistical software (e.g., R, Python, MATLAB, pandas) and database languages.
Applied experience with machine learning on large datasets.

Apple:

We seek proficiency with SQL. Experience with big data technologies such as Hadoop and Spark preferred.
Proficient with Python or R and data visualization tools such as Tableau for full-stack data analysis, insight synthesis and presentation.

Tesla:

• Proficiency in Python, SQL and experience with ML libraries and frameworks like Scikit-learn, h2o or Spark ML.
• Solve complex industrial and technical problems using advanced mathematical modeling and optimization techniques, including but not limited to, big data pre-processing, problem formulation, features engineering, algorithmic selection and evaluation, hyper-parameter tuning for machine learning, and deployment.

Microsoft

• Experience with data analysis and statistical tools (e.g. Python, R, SAS, Matlab or SPSS).

AirbNB

• R/Python; SQL/Hive
• Manipulating large-scale data sets
• Descriptive and predictive modeling

Add Hot Reload to React Rails 6

chowderhead — Fri, 19 Jun 2020 20:08:53 +0000

Photo Cred : Moritz Jager

Assumptions

Your running Rails 6
You have a React Component Entry Point
You have webpack installed in your Rails Repo

Install Dependencies:

yarn add react-hot-loader

Configure Plugins

babel.config.js

...
plugins: [
      'react-hot-loader/babel',
]
...

Modify Root Component

app.js


import React from "react"
import { hot } from 'react-hot-loader'

class App extends React.Component {
  render () {
    return (
      <React.Fragment>
        <Main/>
      </React.Fragment>
    );
  }
}

export default hot(App);

Thats it!

Now we just need to import hot into our base/root component and wrap our export in it.

make sure your running the rails server -p 3000 and also ./bin/webpack-dev-server in another window

This is literally all i needed to do to get hot reloading working for my project.

leave a comment below if you have any issues , and we can try and figure it out!

React Async/Await API layer, with REDUX Sagas

chowderhead — Sun, 23 Feb 2020 09:06:39 +0000

Photo Cred : Naomi Koelemans

Assumptions

you know how to redux
you are familiar with async await
you know a little bit of sagas

I spent at least 4 hours on a saturday night trying to get this to work.

my understanding of async await was shakey at best and i was brand new to sagas - maybe this can help clear the air for you a bit, and give you a practical explanation along with some code examples.

Context

So i have this component here. when a user doesn't enter any information, it is informed by an alertsReducer.js with errors from the backend.

alertsReducer.js


import { Users } from '../constants'
const initialState = {}

export function alertsReducer(state = initialState, action) {

  switch (action.type) {
    case Users.handleUserSignupError:
      return {
        ...state,
        data: action.payload.errors
      }
    default:
      return { data: initialState }
  }
}

As you can see it needs an errors object attached to the payload in order to function correctly.

Cool, now we know what we need to expect - lets go back to the beginning of the request:

In the component it self, i have a simple redux action that gets fired when the user presses submit.

...

handleSubmit = (data) => {
  this.props.handleSubmit(data)
}

render() {
  <div>
    <button onClick={handleSubmit}>submit</button>
  </div>
}

...

totally a watered down example, but you get the idea, it is calling a redux action, and then in the same component, we are reading the state of the alerts reducer, and when we receive the errors, we funnel it back into the component.

Alright - heres where it gets hairy, trying to figure out sagas, while at the same time abstracting out an API layer to make everything nice and neat was my end goal.

establishing an architecture pattern for my redux front end.

/actions.js

export const sendSignupDetails = (data) => {
  return {
    type: Users.sendSignupDetails,
    payload: data
  }
}

export const signupSuccess = (data) => {
  return {
    type: Users.handleUserSignupSuccess,
    payload: data
  };
}

export const signupError = (errors) => {
  return {
    type: Users.handleUserSignupError,
    error: true,
    payload: errors
  };
}

you can see here, when this action fires, it sends the constant action type, and also passes the data into payload!

Okay great , so far so good...

Enter Sagas

I'm not gonna get into all the hairy details that took at least 4 hours of figuring out, ill just do my best to explain my thinking for how i chose this as the best pattern for my sagas setup...

/sagas.js


function* sendSignupDetails(action) {
  yield takeLatest(Users.sendSignupDetails, postUserSignup)
}

function* postUserSignup(action) {
  const response = yield call(usersApi.signupUser, action.payload);
  if (response.errors) {
    yield put(signupError(response));
  } else {
    yield put(signupSuccess(response))
  }

}

function* handleUserSignupSuccess(action){
  yield takeLatest(Users.handleUserSignupSuccess, redirectToDashboard)
}

of course there is some more setup involved, but I wanted to focus mainly on the sagas themselves....

as you can see sendSignupDetails is a watcher, that waits for the correct action to be dispatched, in this case it's the one we set up earlier which is : Users.sendSignupDetails

using takeLatest it will watch for the latest call of the action. you can google more info on takeLatest all over the interwebs.

the first argument of takeLatest is the action itself, and the second, is what you want it to do when it sees this action.

here i am calling function* postUserSignup , this does a few things.

yield is calling an API request for us, and passing our payload into the Api request. we set this equal to a response so that we can grab the insides out and dispatch them off into either a success or error , based on the contents of the response.

notice if(response.errors) there are probaly better ways to do this, but if our server returns an errors key, which i have it doing , then we will dispatch the signupError action we set up earlier in the actions file.

if you can tell me how to get this work with try catch please leave comments below, otherwise, do this, cause it has served me well thus far.

API File

since im a big fan of abstracting things out and making them nice and neat, i have an /API file that i import into my sagas file, its basically a class that looks like this:

/API.js

// notice how i am extending a base class here
import Api from './base-api';

export class UsersAPI extends Api {
  signupUser = async (data) => {
    const { user } = data;
    let res = await Api.postRequest('/users', {user})
    let response = await res.json()
    return response
  }
}

great, so to make this as neat as possible , I am using async on the function definition, and then to handle the responses from the actual API requests , I am using await, and then using await again to render json out of the initial response.

/base-api.js


const headers = {
  'Accept': 'application/json',
  'Content-Type': 'application/json'
}

export default class Api {
  static postRequest = (url, data) => {

    const options = {
      method: 'POST',
      headers: headers,
      body: JSON.stringify(data)
    }

    return fetch(url,options)
  }
}

I have all of my crud options CREATE,READ,UPDATE,DELETE seprated into neat little methods on my base class to clean it up even more.

THATS IT !

the static method on the base class will return an fetch request to my /API.js file
In /API.js I will handle the response from the API using async/await
once i have a nice clean JSON response, i will send it back to the saga
where it will call the action, and pass the payload into the reducer.

Maybe, just maybe this will save someone some time- it took me along time to figure out a pattern that worked, since everything i typed async/await in google, it would show me articles like :

ASYNC AWAIT VS REDUX SAGAS

hahahhaa, well thats all for now, until we meet again! happy coding!!!

Ken

Rails Password Validation

chowderhead — Sun, 16 Feb 2020 05:52:31 +0000

Photo Cred: @breadboyshaun

FEBRUARY 15, 2020

Validate Users Model Password

Alright lets re invent the wheel for funsies. Why? because its saturday night and i want something fun to do- before all these people come trash my house.

Assumptions

you know how to Ruby on Rails
you have a user model set up with a password field

Why?

I wrote this because i wanted to throw individual errors for each requirement they were missing. Thank you stack overflow ! I didn't cite my sources , but check the stack youll find them.

What we are building:

Code

class User < ApplicationRecord
  validate :password_lower_case
  validate :password_uppercase
  validate :password_special_char
  validate :password_contains_number

  def password_uppercase
    return if !!password.match(/\p{Upper}/)
    errors.add :password, ' must contain at least 1 uppercase '
  end

  def password_lower_case
    return if !!password.match(/\p{Lower}/)
    errors.add :password, ' must contain at least 1 lowercase '
  end

  def password_special_char
    special = "?<>',?[]}{=-)(*&^%$#`~{}!"
    regex = /[#{special.gsub(/./){|char| "\\#{char}"}}]/
    return if password =~ regex
    errors.add :password, ' must contain special character'
  end

  def password_contains_number
    return if password.count("0-9") > 0
    errors.add :password, ' must contain at least one number'
  end
end

We use errors.add to add message to the error messages on attribute. More than one error can be added to the same attribute. If no message is supplied, :invalid is assumed. ruby docs

Notice we have some sick regular expressions i got off stack overflow

in each of these validations, we return if the field matches the requirements, if it doesn't we use errors.add to bubble up some sick errors.

This shit is so open source, please use as you will my friends!

happy coding!

DEV Community: chowderhead

JEV assisted LLM Trading

We gave our LLM trading bot an auditor that never sees the chart

The bot, briefly

What JEV is

The shadow harness

The switch is currently off

What we expect the data to answer

What it can't do

Engram: A new type of AI

⏺ How Engram Actually Thinks: A Deep Dive Into Its Reasoning Engine

The Attention Brain: A Panel of Pattern-Matching Judges

Words Are Coordinates in Space (Yes, Really)

Attention: The Smart Way to Look Backwards

Query, Key, Value (The Spotlight Mechanism)

The Feedforward Network (The Actual Thinking Part)

Residual Connections (The Memory Trick)

The 4-Layer Journey: How Understanding Deepens

N-gram Memory Injection (The Shortcut)

Pondering: Thinking Harder When Confused

The Final Prediction: From Vector to Word

Episodic Memory: The Experience Boost

Why This Architecture Is Different

The Whole Pipeline, Start to Finish

What Makes This Research-Worthy

Trust the Server, Not the LLM: A Deterministic Approach to LLM Accuracy

🎯 The problem

TL;DR

⚠️ Why models get numbers wrong

🏗️ Architecture

🔧 Layer 1: Fat tool server (compute everything)

📝 Layer 2: Provenance (seal the payload)

📄 Layer 3: Deterministic rendering

🗄️ Layer 4: Exact entity grounding

✅ Layer 5: Deterministic verifier

Where a model judge still helps

🔄 Layer 6: Bounded repair, fail closed

🚨 Trends and dispersion: computed, not narrated

🧱 The boundary: what this guarantees and what it doesn't

🤔 So why use an LLM at all?

📏 Measuring it

📊 Summary

📚 References

Self Writing Lang Graph State

Install AUTO-GPT on mac OS (march 2024)

References:

Changing (dark/light) Image on Ionic 5 react ios app

Teh struggle is real...

Component:

Css Stylesheet (for Component)

Another way...

Setup up a CRYPTO Balance widget on IOS with Node and scriptable

About me: https://kenchambers.dev

Enjoy!

references:

Code:

assumptions:

- you have heroku CLI and you are logged in, if not click here

Setting up your repo:

Deploy to heroku

Creating a route to return BTC Price

Scriptable Widget:

Setting up Redux Toolkit and React Router

About me: https://kenchambers.dev/

create a new redux tool kit app with create-react-app

update RTK boilerplate code

Setup Routing:

WE DID IT

Data Scientist Tools

Quick list of Tooling required by large companies, when sourcing a few job listings ( November 6 )

listed for my own personal use.

Blizzard:

Amazon:

Google:

Apple:

Tesla:

Microsoft

AirbNB

Add Hot Reload to React Rails 6

Assumptions

create a new redux tool kit app with `create-react-app`