DEV Community: subhansh

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subhansh — Thu, 23 Jul 2026 15:12:45 +0000

Jul 23

I Benchmarked 19 Retrieval Pipelines Head-to-Head and the Results Were Surprisingly Honest

3 min read

I Benchmarked 19 Retrieval Pipelines Head-to-Head and the Results Were Surprisingly Honest

subhansh — Thu, 23 Jul 2026 15:09:01 +0000

I got tired of retrieval papers claiming their pipeline is "state-of-the-art" without significance tests, without telling you what dataset they ran on, and without reproducible code. So I built raven-retrieval -- 19 retrieval pipelines, real BEIR datasets, Bonferroni-corrected bootstrap significance tests, all the numbers whether they look good or not. MIT licensed, open source.

What It Does

The core question: does ColBERT-style late interaction scoring at every level of a RAPTOR hierarchical tree actually improve retrieval over simpler approaches? To answer that properly you need the same datasets, same chunking, same metrics, same significance tests for every pipeline. Otherwise you're comparing apples to oranges.

The framework implements 19 pipelines and benchmarks them all on BEIR datasets (SciFact, HotpotQA) on equal footing. No cherry-picking.

The 19 Pipelines (Quick Summary)

Core: Naive Dense RAG, Hybrid RAG (BM25+Dense+RRF), ColBERT Late Interaction, RAPTOR+Late Interaction (novel combination).

Research (2024-2025): HyDE, SPLADE, SPLADE+Dense, BM25+Rocchio PRF, Contextual Retrieval, Contextual Hybrid, Late Chunking, RAPTOR+Late Traversal, Agentic Multi-Hop, Reflection Retriever, Graph Retrieval, Two-Stage Dense+Reranker, Approximate Late Interaction, Contextual Dense, Contextual BM25.

Composition modules: Cross-Encoder Reranker, Two-Stage Retriever, Document Graph, Residual Compressor (~30x storage reduction).

The novel one is RAPTOR + Late Interaction: build a RAPTOR hierarchical summary tree, then apply ColBERT MaxSim scoring at every node (leaf chunks AND summary nodes). Nobody has published on this combination before.

How MaxSim Works

ColBERT's scoring: for each query token, find the most similar document token (cosine), then sum those maximums across all query tokens. Every query token gets a vote for its best-matching document token. This captures fine-grained token-level relevance that single-vector cosine misses entirely. Implemented in pure numpy, no torch needed for scoring.

The Benchmark Results

Run on Google Colab T4 GPU, seed=42, two datasets.

SciFact (100 queries, single-hop scientific claims)

Pipeline	nDCG@10	Per-Query
HyDE	0.712	19ms
Naive Dense	0.696	32ms
Contextual Hybrid	0.682	93ms
Hybrid RAG	0.667	87ms
BM25+PRF	0.529	43ms

Key finding: Top 4 are statistically indistinguishable (all pairwise p > 0.05 after Bonferroni). BM25+PRF is significantly worse (p ~ 0.000). The "winner" HyDE is only 2 points above Dense -- within noise.

HotpotQA (50 queries, multi-hop reasoning)

Pipeline	nDCG@10	Per-Query
Hybrid RAG	0.925	22ms
Contextual Hybrid	0.923	22ms
Naive Dense	0.906	12ms
HyDE	0.892	26ms
BM25+PRF	0.869	8ms

The ranking flips completely. HyDE drops from 1st to 4th. Hybrid RAG wins because BM25+Dense fusion catches different reasoning hops. A single hypothetical document can't bridge two separate reasoning steps.

The most important finding: No single pipeline dominates across task types. Single-hop favors semantic (HyDE). Multi-hop favors hybrid. Anyone claiming universal superiority hasn tested on enough datasets.

The Bugs I Found

Before v0.3, 13 bugs (6 critical) silently corrupted results:

ColBERT matched against PAD tokens -- attention mask wasn used, padding tokens got MaxSim votes alongside real content
RAPTOR clustering used wrong embedding space -- centroids from ColBERT means while scoring with SBERT vectors
Per-query nDCG was all zeros -- BEIR evaluate() returns averaged floats, not per-query; significance tests fed zeros and reported "no difference" everywhere (false negatives)
Reflection evaluated keyword coverage over doc ID strings ("doc_1", "doc_42") instead of actual text, triggering endless reformulation loops
RAPTOR pipelines crashed -- UMAP bug when local clusters had 3 samples (k >= N), burned 189 minutes of Colab time

These don crash your program. They just make your results wrong in ways you can see from final numbers alone.

Known Limitations

ColBERT projection head is Xavier-initialized (untrained). Late interaction scores ~0.58 without training, worse than Dense at 0.696. Training support exists (ColbertContrastiveEncoder with InfoNCE) but no trained checkpoint in default benchmark yet.
GMM soft-clustering converges to near-hard assignment on ~100-token chunks (matches Stanford CS224N reproduction).
Late Chunking limited by BERT's 512-token window -- needs long-context model to really shine.
HotpotQA scores are on 2,000-doc subsample, not the full 5.2M corpus. Real retrieval is harder than these numbers suggest.

git clone https://github.com/subhansh-dev/raven-retrieval.git
cd raven-retrieval
pip install -e ".[full]"
python run_enhanced_benchmark.py --dataset scifact --top-k 10 --seed 42

CLI entry points: raven-benchmark, raven-report. Low-RAM? Use --max-docs. Want trained ColBERT? Use --colbert-checkpoint. All 55 tests pass.

Why This Matters

Retrieval benchmarks have a credibility problem. Papers claim improvements without significance tests, without multiple datasets, without reproducible code. Raven-retrieval fixes all of that. The honest answer -- that top pipelines are tied, that rankings flip between datasets, that BM25+PRF is bad on scientific text -- is more useful than cherry-picked claims of universal superiority.

github.com/subhansh-dev/raven-retrieval | subhansh.dev

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subhansh — Sat, 18 Jul 2026 13:54:05 +0000

subhansh

Jul 12

I built a programming language because C++ almost killed my drone

#programming #rust #robotics #compilers

5 min read

I Built a Full Raft Consensus Engine in Rust at 17 — Here's Every Gritty Detail

subhansh — Fri, 17 Jul 2026 16:24:04 +0000

I Built a Full Raft Consensus Engine in Rust at 17 — Here's Every Gritty Detail

I'm 17. This is my first distributed systems project.

Raft is the consensus algorithm that powers etcd, Consul, TiKV, and a dozen other production systems. The paper makes it look straightforward — leader election, log replication, snapshots. But implementing it from scratch? Every paragraph in the paper expands into dozens of edge cases. Figure 8 alone (the "previous term entries can't be committed directly" rule) caused me three rewrites.

This implementation is 4,400+ lines of Rust across 7 crates, 52 tests, real gRPC networking, disk persistence with sled, log compaction via InstallSnapshot, and a chaos harness that kills nodes and partitions networks. No etcd code. No TiKV dependencies. Pure paper-to-production.

What This Actually Is

A complete, tested Raft implementation that you can run right now:

# In-process 3-node demo
cargo run --example kv-demo

# Or run a real cluster across 3 terminals
cargo build --release
# Terminal 1: cargo run --release -p raft-node -- --node-id 1 --peers 2@127.0.0.1:8081,3@127.0.0.1:8082 --port 8080
# Terminal 2: cargo run --release -p raft-node -- --node-id 2 --peers 1@127.0.0.1:8080,3@127.0.0.1:8082 --port 8081
# Terminal 3: cargo run --release -p raft-node -- --node-id 3 --peers 1@127.0.0.1:8080,2@127.0.0.1:8081 --port 8082

What works:

Leader election with randomized timeouts (300-500ms) and proper log-up-to-date checks
Log replication with Figure 8 compliance — previous-term entries commit indirectly only
Fast backup optimization — followers return conflict_term/conflict_index, leader skips entire terms in one RTT
Persistence before RPC replies (Figure 2 requirement) — current_term, voted_for, log entries flushed to sled
Snapshotting with InstallSnapshot RPC — log compaction, boundary handling, 15 tests
Chaos testing — network partitions, node kills, leader isolation, process-level chaos with 5 real OS processes

What's not done (honest gaps):

Dynamic membership changes (joint consensus)
ReadIndex/LeaseRead for linearizable reads
WAN optimization / batching
WAL-based persistence (sled works but isn't crash-consistent like a WAL)

Architecture — 7 Crates, Clean Boundaries

raft/
├── Cargo.toml
├── crates/
│   ├── raft-core/          # Pure logic — 40 unit tests
│   │   ├── types.rs
│   │   ├── state.rs
│   │   ├── election.rs
│   │   ├── replication.rs
│   │   └── snapshot.rs
│   ├── raft-rpc/           # gRPC transport (tonic)
│   ├── raft-storage/       # Persistence abstraction
│   │   ├── RaftStorage trait
│   │   ├── SledStorage
│   │   └── MemStorage
│   ├── raft-node/          # Standalone binary
│   └── raft-chaos/         # Chaos harness
└── examples/
    ├── kv-demo/
    └── process-chaos/

Why this separation matters: The core logic has zero I/O. No network, no disk, no time. Pure functions. Unit tests are deterministic and fast. Storage backends are swappable without touching consensus logic. The chaos harness injects failures at the network layer — not the logic layer.

Leader Election — The Part Everyone Gets Wrong

The paper says: "If election timeout elapses without receiving AppendEntries, become candidate." Simple, right?

Wrong. Here's what actually happens in election.rs:

pub fn on_election_timeout(&mut self) -> Vec<NodeAction> {
    self.become_candidate();  // term++, vote for self

    let args = RequestVoteArgs {
        term: self.current_term,
        candidate_id: self.id,
        last_log_index: self.last_log_index(),
        last_log_term: self.last_log_term(),
    };

    self.peers.iter()
        .map(|&peer| NodeAction::SendRequestVote(peer, args.clone()))
        .collect()
}

But the vote granting logic is where bugs live (§5.4.1):

fn is_log_up_to_date(&self, candidate_last_term: u64, candidate_last_index: u64) -> bool {
    let my_last_term = self.last_log_term();
    let my_last_index = self.last_log_index();

    if candidate_last_term != my_last_term {
        candidate_last_term > my_last_term  // Higher term wins
    } else {
        candidate_last_index >= my_last_index  // Same term? Longer log wins
    }
}

The subtlety that catches everyone: A candidate with term 3 and log length 1 beats a follower with term 2 and log length 100. Term always wins. This prevents the "stale leader with massive log" problem.

My test suite hammers this:

12 election tests covering grants, rejections, stale terms, split votes, term conversion
test_no_two_leaders_same_term spins up 5 nodes simultaneously — all become candidates, all vote for themselves, none win (majority = 3, each has 1 vote). Beautiful.

Log Replication — Where Figure 8 Bites You

Leader receives command → appends to local log → sends AppendEntries → majority replicates → commit → apply. Standard stuff.

Except Figure 8. The paper shows a scenario where a leader commits an entry from a previous term, then crashes. New leader doesn't have that entry. Data loss.

The rule: A leader can only directly commit entries from its current term. Previous-term entries commit indirectly when a current-term entry after them commits.

My implementation in replication.rs:

pub fn advance_commit_index(&mut self) {
    if self.state != NodeState::Leader { return; }

    let mut match_indices: Vec<u64> = self.match_index.values().copied().collect();
    match_indices.push(self.last_log_index());
    match_indices.sort_unstable_by(|a, b| b.cmp(a));

    for &n in &match_indices {
        if n > self.commit_index {
            if let Some(entry) = self.get_entry(n) {
                // FIGURE 8 RULE: only commit current-term entries directly
                if entry.term == self.current_term {
                    let replicated = match_indices.iter().filter(|&&idx| idx >= n).count();
                    if self.is_majority(replicated) {
                        self.commit_index = n;
                        break;
                    }
                }
            }
        }
    }
}

The test that proves it works (test_advance_commit_index_figure8):

// Leader has: index 1 (term 1), index 2-3 (term 2)
// Followers replicated up to index 1 (term 1 entry)
leader.match_index.insert(2, 1);
leader.match_index.insert(3, 1);
leader.advance_commit_index();
// commit_index stays 0 — term 1 entry NOT committed directly

// Now followers replicate index 2 (term 2)
leader.match_index.insert(2, 2);
leader.match_index.insert(3, 2);
leader.advance_commit_index();
// commit_index = 2 — term 2 entry commits, drags term 1 along indirectly

This test alone caught 3 bugs in my early implementation. Write the Figure 8 test first. Always.

Fast Backup — The Optimization That Makes Raft Practical

Without fast backup, a follower 10,000 entries behind takes 10,000 round-trips to catch up. The paper's appendix describes the fix: followers return conflict_term and conflict_index on rejection. Leader jumps next_index past the entire conflicting term.

My implementation:

pub fn handle_append_entries_reply(
    &mut self,
    peer: NodeId,
    reply: AppendEntriesReply,
) -> Option<NodeAction> {
    if reply.success {
        // Update match_index, next_index normally
        ...
    } else {
        // FAST BACKUP
        if let Some(conflict_term) = reply.conflict_term {
            let mut new_next = reply.conflict_index.unwrap_or(1);
            for entry in self.log.iter().rev() {
                if entry.term == conflict_term {
                    new_next = entry.index + 1;  // Jump PAST the term
                    break;
                }
            }
            self.next_index.insert(peer, new_next.max(1));
        } else if let Some(conflict_index) = reply.conflict_index {
            self.next_index.insert(peer, conflict_index.max(1));
        } else {
            // Fallback: decrement by 1 (slow path)
            let current = self.next_index.get(&peer).copied().unwrap_or(1);
            self.next_index.insert(peer, (current - 1).max(1));
        }
        ...
    }
}

Test proof (test_fast_backup_convergence):

Leader has terms: [1, 1, 3, 5] at indices [1, 2, 3, 4]
Follower rejects at index 4, says "I have term 1 at index 4, first index of term 1 is 1"
Leader jumps next_index from 5 → 3 (skips all of term 1 in one round-trip)

Real-world impact: A node 1M entries behind catches up in ~log(terms) round-trips, not O(entries). This is the difference between "toy" and "production."

Persistence — The "Before Reply" Rule

Figure 2 is explicit: persist before responding. My grpc_server.rs enforces this:

async fn request_vote(&self, request: Request<ProtoRequestVoteArgs>) -> ... {
    let core_args = proto_to_core_request_vote_args(args);
    let mut node = self.state.node.write().await;
    let reply = node.handle_request_vote(core_args);

    // PERSIST BEFORE REPLY — Figure 2 requirement
    let _ = self.state.storage.save_current_term(node.current_term);
    let _ = self.state.storage.save_voted_for(node.voted_for);

    Ok(Response::new(core_request_vote_reply_to_proto(reply)))
}

async fn append_entries(&self, request: Request<ProtoAppendEntriesArgs>) -> ... {
    let core_args = proto_to_core_append_entries_args(args);
    let mut node = self.state.node.write().await;
    let reply = node.handle_append_entries(core_args);

    // Persist log entries BEFORE responding
    if reply.success {
        for entry in &node.log {
            if entry.index >= core_args.prev_log_index {
                let _ = self.state.storage.save_log_entry(entry);
            }
        }
    }
    let _ = self.state.storage.save_current_term(node.current_term);
    Ok(Response::new(core_append_entries_reply_to_proto(reply)))
}

SledStorage uses bincode serialization with a 64MB cache. Each write flushes to disk. On restart, the node loads current_term, voted_for, and the full log — resumes exactly where it left off.

Crash recovery test: Kill a node mid-replication, restart it, verify it has the same committed state. Works.

Snapshotting — Log Compaction Done Right

Logs grow forever. Snapshots fix this. Figure 12 + Figure 7.

Leader side (state.rs):

pub fn snapshot(&mut self, include_index: u64) -> Option<(Vec<u8>, u64, u64)> {
    // Validations: must be committed, applied, past last snapshot
    if include_index <= self.last_included_index
        || include_index > self.commit_index
        || include_index > self.last_applied {
        return None;
    }

    // Collect all applied commands up to include_index
    let mut data = Vec::new();
    for entry in &self.log {
        if entry.index <= include_index {
            data.extend_from_slice(&entry.command);
            data.push(b'\n');
        }
    }

    let last_included_term = self.get_entry(include_index)
        .map_or(self.last_included_term, |e| e.term);

    // Discard compacted entries
    self.log.retain(|e| e.index > include_index);
    self.last_included_index = include_index;
    self.last_included_term = last_included_term;

    if self.last_applied < include_index {
        self.last_applied = include_index;
    }

    Some((data, include_index, last_included_term))
}

Follower side (handle_install_snapshot):

pub fn handle_install_snapshot(&mut self, args: InstallSnapshotArgs) -> InstallSnapshotReply {
    // Rule 1: Stale term → reject
    if args.term < self.current_term { ... }

    // Rule 2: Higher term → become follower
    if args.term > self.current_term { self.become_follower(args.term); }

    // Rule 3: Snapshot behind our snapshot → reject (stale)
    if args.last_included_index <= self.last_included_index { ... }

    // Rule 4: Install it
    let existing_term = self.get_entry(args.last_included_index).map(|e| e.term);

    // If boundary matches, keep entries after; else discard ALL log
    if existing_term == Some(args.last_included_term) {
        self.log.retain(|e| e.index > args.last_included_index);
    } else {
        self.log.clear();
    }

    self.last_included_index = args.last_included_index;
    self.last_included_term = args.last_included_term;

    // Advance commit/applied past snapshot
    if self.last_applied < args.last_included_index {
        self.last_applied = args.last_included_index;
    }
    if self.commit_index < args.last_included_index {
        self.commit_index = args.last_included_index;
    }

    InstallSnapshotReply {
        term: self.current_term,
        reset_election_timer: true,
        snapshot_data: Some(args.data),
        last_included_index: Some(args.last_included_index),
    }
}

15 snapshot tests cover: basic compaction, stale rejection, uncommitted rejection, unapplied rejection, boundary preservation, empty log after compact, InstallSnapshot basic, stale term/index rejection, log discard, matching boundary kept, mismatched boundary discarded, term_at_index with snapshot, apply after snapshot, multiple snapshots.

Chaos Testing — Breaking It On Purpose

Unit tests verify correctness. Chaos tests verify resilience.

raft-chaos spins up real nodes (in-process or real processes), then:

Partitions networks bidirectionally (HashSet<(NodeId, NodeId)>)
Kills nodes (clears peers, sets to follower)
Heals partitions
Verifies safety (no split-brain) and liveness (new leader elected)

The 5 chaos tests:

Test	What It Verifies
`test_chaos_leader_election`	3-node cluster elects leader within 5s
`test_chaos_command_submission`	Commands replicate to all nodes' state machines
`test_chaos_node_survives_majority`	Kill leader → new election in majority partition
`test_chaos_partition_minority_cannot_commit`	2-node minority partitioned → no commits, majority continues
`test_chaos_partition_leader_isolated`	Isolate leader → majority elects new leader → heal → old leader steps down

Process chaos (examples/process-chaos): Spawns 5 real OS processes via cargo run --release -p raft-node, kills the leader process, verifies new election. This catches bugs the in-process harness misses (file descriptor limits, port binding races, sled locking).

The KV Demo — See It Run

cargo run --example kv-demo

Output:

=== 3-node Raft cluster started ===
Nodes on ports [9001, 9002, 9003]
Waiting for leader election...
Leader elected: node 2
Submitted: "name Alice" -> index=1, term=1
Submitted: "name Bob" -> index=2, term=1
Submitted: "lang Rust" -> index=3, term=1
Submitted: "lang Python" -> index=4, term=1

=== Cluster state ===
Node 1: state=Follower, term=1, log_len=4, commit=4, applied=4, store={"name": "Bob", "lang": "Python"}
Node 2: state=Leader, term=1, log_len=4, commit=4, applied=4, store={"name": "Bob", "lang": Python"}
Node 3: state=Follower, term=1, log_len=4, commit=4, applied=4, store={"name": "Bob", "lang": "Python"}

All nodes consistent: true

Real consensus. Real replication. Real state machines.

Spec Reference

Built from Ongaro & Ousterhout's "In Search of an Understandable Consensus Algorithm" (2014) — the Raft paper. Every field name, every rule, follows Figure 2 directly.

Test plan follows MIT 6.5840 (Distributed Systems) Raft lab structure — each stage isolates a bug class:

Stage A: Leader election
Stage B: Log replication
Stage C: Persistence + crash recovery
Stage D: Log compaction / snapshotting
Stage E: Chaos testing

Links

GitHub: https://github.com/subhansh-dev/raft-rs

Email: me@subhansh.dev
portfolio: https://subhansh.dev

I built a programming language because C++ almost killed my drone

subhansh — Sun, 12 Jul 2026 19:00:41 +0000

I'm 17. Last year I was writing drone firmware in C++ and Python. One afternoon my ultrasonic sensor returned null during a landing sequence. The code didn't check for it. The drone tried to divide by zero, the motor controller panicked, and my DJI Tello-sized quadcopter dropped from 3 meters onto concrete.

That was a $400 lesson in why runtime errors in robotics aren't just bugs. They're physics problems.

So I built a programming language.

The problem nobody talks about

When you're writing a web app and something crashes, you reload the page. When you're writing drone code and something crashes, the drone crashes. Into whatever's below it. Usually something expensive or fragile or alive.

C++ and Python dominate robotics. Both assume you'll handle edge cases yourself. Sensor returns null? That's your problem. Timing deadline missed? Good luck. No fallback path for when the lidar fails over water? Figure it out.

ROS (Robot Operating System) helps with some of this, but it's an entire infrastructure layer. What I wanted was something simpler: a language where the compiler catches the stuff that kills robots before you ever compile it.

Here's what I mean. In C++, you write:

float distance = sensor.read();
float speed = target_distance / distance;
motor.set(speed);

If distance is 0, you get a division by zero. If sensor.read() returns NaN, you get NaN propagation through your motor controller. The compiler doesn't care. It'll happily compile this and let your drone fall out of the sky.

In Fabric (the language I built), you'd write:

sensor distance: Sensor<f32, ±0.02>
actuator motor: Motor

loop {
    let speed = target_distance / distance
    motor.write(speed)
}

If you try to use distance without checking that the sensor returned a valid reading, the compiler throws an error. If distance could be zero and you didn't add a fallback path, the compiler throws an error. If your sensor has an uncertainty bound of ±0.02 and you're doing math that could amplify that uncertainty past a safety threshold, the compiler throws an error.

The compiler knows about physics. Not perfectly, but enough to catch the stuff that actually kills robots.

How it works under the hood

I wrote the whole thing in Rust. About 4,500 lines across 8 crates. Here's the pipeline:

.fab source → Lexer → Parser → AST → Type Checker → Fallback Graph → IPET Timing → CodeGen → .py / .c

Each step catches a different category of mistakes.

The type system tracks sensor uncertainty as a first-class concept. When you declare Sensor<f32, ±0.02>, that ±0.02 isn't decoration. It propagates through every math operation. Add two sensors with ±0.02 uncertainty and the result has ±0.04. Multiply by a constant and the uncertainty scales accordingly. If the final result's uncertainty exceeds what's safe for your actuator, the compiler catches it.

The fallback graph checks that every sensor has a plan B. Not just declared, but actually reachable. If your fallback function A calls fallback function B, and B calls A, that's a cycle. The compiler detects it. If sensor X has no fallback at all, the compiler refuses to build.

IPET timing analysis uses an Integer Linear Program to prove worst-case execution time at compile time. It builds a control flow graph, assigns cycle costs to each instruction (ARM Cortex-M4 model: ALU = 1 cycle, float division = 14 cycles, sensor read = 2 cycles), and solves for the maximum possible execution time across all code paths including loops.

I didn't come up with this technique. It's from Li & Malik's 1994 paper on implicit path enumeration. But I implemented it in Rust using a pure-Rust ILP solver called good_lp with the microlp backend. No external solver dependency. No C FFI. Just Rust solving linear programs.

The solver maximizes sum(cost_i * x_i) subject to flow conservation constraints. If the solver fails (which happens on really complex control flow), the system falls back to a conservative estimate instead of crashing.

The two-target code generation

Fabric compiles to two backends: Python for Webots simulation, and C for ARM Cortex-M hardware.

Python output generates a Webots controller class with sensor initialization, motor setup, fallback state tracking, and all the logic. You can test your drone code in simulation before touching hardware.

C output generates code that includes a hal.h hardware abstraction layer. Static sensor handles, #define TIMEOUT_MS for fallback deadlines, proper C types (float, int), and deadline enforcement using hal_get_time_ms().

Same source file, two completely different targets. No #ifdef in your code. No conditional compilation. The compiler handles the target-specific stuff.

What I'd do differently

The parser is hand-rolled. About 700 lines of Pratt parser with recursive descent for statements. I started with chumsky (a Rust parser combinator library) but their API changed between versions and the documentation was sparse when I needed it most. So I wrote my own.

It's fine. But it means every time I want to add a new syntax construct, I'm writing parser code by hand. A proper parser generator would've saved time in the long run, even with the initial investment.

The loop bound estimation is a heuristic. If your loop body has 1-3 statements, the compiler assumes it runs 10 times. 4-6 statements? 5 times. 7+? 3 times. This is obviously wrong for real programs. Proper static bound analysis would make the IPET results much more accurate. It's on my list.

The C output references a clamp() function that it never defines. If you actually try to compile the generated C code on hardware, you'll get a linker error. I know about it. I just haven't fixed it yet.

Why I'm publishing this

I put all 8 crates on crates.io. The lexer, parser, type checker, everything. The repo is at github.com/subhansh-dev/fabric.

Partly because I want other people to try it. If you're working on robotics or drones and you've been bitten by runtime errors, Fabric might save you some pain.

Mostly because I think the idea matters more than my implementation. The safety-critical systems world has had formal methods and static analysis for decades. But it's all locked behind expensive tools and enterprise licenses. I wanted to build something that a 17-year-old with a Raspberry Pi could use.

Is my implementation perfect? No. The IPET loop bounds are guesses. The C backend doesn't handle drone swarms. The match expression codegen references some variables it doesn't always generate. I know about all of these. They're bugs, not features, and I'll fix them.

But the core idea is sound: compile-time safety for robotics shouldn't require a $50,000 license for Simulink. Sometimes the right answer to "how do we prevent this bug" isn't "write better tests" or "add more runtime checks." Sometimes it's "make the compiler say no."

What's next

I'm working on adding hardware-in-the-loop testing support. The idea is that Fabric could instrument your actual hardware and compare runtime sensor readings against the compile-time uncertainty bounds. If reality diverges from what the compiler predicted, you get a warning before your next flight.

Also working on proper static loop bound analysis. The current heuristic is fine for demos but useless for real deployments. I'm looking at abstract interpretation techniques to actually prove bounds instead of guessing.

If any of this sounds interesting, check out the repo. Open an issue. Try it on your own robot. Break it and tell me how.

That's how this gets better.

Fabric is MIT licensed. The crates are published on crates.io. The docs are in the README because I'm 17 and haven't written proper docs yet.

gh repo link :- https://github.com/subhansh-dev/fabric

[Boost]

subhansh — Wed, 08 Jul 2026 17:48:55 +0000

subhansh

Jul 8

I Built a Free Toolkit That Makes Any AI Coding Agent Write Better Code

#ai #productivity #opensource #agentskills

4 min read

I Built a Free Toolkit That Makes Any AI Coding Agent Write Better Code

subhansh — Wed, 08 Jul 2026 17:46:45 +0000

I Extracted 95+ Skills From Claude Fable 5, GPT-5.5, and Gemini CLI Into One Free Repo

Your AI coding agent is powerful but generic. It writes code with purple gradients, opens responses with "Great question!", and ships skeletons instead of working code.

I fixed that.

Agent Maxxing is a free, open-source collection of 95+ production-ready skills, 19 UI components, and 7 system prompts — extracted from the leaked system prompts of the world's best AI agents.

The Problem

Every AI coding agent has the same personality:

Starts every response with praise ("Great question!", "Absolutely!")
Writes code with no error handling ("Here's a skeleton, you fill it in")
Uses purple-to-blue gradients everywhere
Sounds like a corporate press release
Asks permission for low-risk actions instead of just doing them

The root cause? Their system prompts are generic. They don't know your design system, your coding standards, or how to sound like a human.

The Solution

I extracted the actual system prompts from:

Claude Fable 5 (3,826 lines) — personality, memory system, tone, refusal handling
GPT-5.5 Codex (11,104 lines) — engineering judgment, frontend rules, formatting
Gemini CLI (254 lines) — context efficiency, sub-agent orchestration
Claude Code (1,798 lines) — tool usage, code review methodology, agent delegation

Then I organized everything into 9 skill categories with 95+ individual skill files.

What's Inside

Engineering (22 skills)

Deep Code Review — 8-angle methodology from Claude Code bundled skills
Security Review — Senior security engineer audit with false-positive filtering
Execution Protocol — "Solve it, don't ask about it" problem-solving chain
Debugging Patterns — Systematic methodology for finding and fixing bugs
API Design — REST patterns, status codes, pagination
Database Patterns — Schema design, indexing, migrations
Performance Patterns — Lazy loading, caching, memoization

Frontend Design (16 skills)

Anti-Patterns — What makes AI output look AI-generated
Artifact Design — Deliberate design choices from Claude Code
Motion Design — Timing, easing, enter/exit patterns
Typography — Font pairing, scale, typographic systems
Responsive Design — Breakpoints, fluid layouts

UI Components (19 patterns)

Glass Card, Gradient Button, Modal Dialog, Tabs, Dropdown, Avatar, Progress Bar, Badge, Toggle Switch, Accordion, Tooltip, Navbar, Data Table, Loading Skeleton, Toast Notification, Breadcrumb, Pagination, Animated Input

System Prompts (7 modules)

Extracted and intelligently curated from leaked prompts:

Agent Core Personality — From Claude Fable 5
Coding Excellence — From GPT-5.5 Codex
Reasoning & Planning — From Codex Plan Mode
Frontend Mastery — From GPT-5.5 + Claude Design
Agent Orchestration — From Claude Code + Gemini CLI
Tone & Communication — From Fable 5 + Codex + Cursor

How It Works

Installation

# Clone the repo
git clone https://github.com/subhansh-dev/agent-maxxing.git

# Install for your agent
cp -r agent-maxxing ~/.claude/skills/agent-maxxing  # Claude Code
cp -r agent-maxxing ~/.codex/skills/agent-maxxing   # Codex CLI
cp -r agent-maxxing .cursor/skills/agent-maxxing    # Cursor

The Lazy Prompt

Paste this to your agent and it self-fine-tunes:

Deep dive into the agent-maxxing folder. Read every .md file — every skill, every system prompt, every component, every workflow example. Understand what each file teaches. Then fine-tune yourself: adopt the patterns, internalize the anti-patterns, apply the engineering judgment, use the writing style. Integrate all 95+ skills so you can use them on any task. From now on, before responding to anything, check if a relevant skill exists and apply it.

How Agent Self-Fine-Tuning Works

It's not weight training — it's context injection. When an agent reads a skill file:

The file content enters the agent's context window
The agent internalizes the patterns for that session
It applies those patterns to every subsequent response
The behavior changes without retraining

The more skills it reads, the better it gets. The FINE-TUNE-AGENT.md walks the agent through every skill category in order.

Key Anti-Patterns (What Agents Stop Doing)

After fine-tuning, your agent will:

Stop opening with "Great question!" or "Absolutely!"
Stop using purple-to-blue gradients in every design
Stop shipping skeleton code ("Here's a basic implementation")
Stop hedging with "It seems like..." or "It appears that..."
Stop asking permission for low-risk actions
Start writing code with proper error handling
Start matching the existing codebase style
Start sounds like a human, not a press release
Start reviewing code with 8-angle methodology
Start shipping working code, not skeletons

The Golden Rules

Every top agent in this collection follows these:

Search before answering — Never guess when you can check
Read the codebase first — Don't assume, investigate
Parallel tool calls — Independent operations run simultaneously
Match existing patterns — Follow the repo's conventions
Show, don't tell — Demonstrate with examples
Be concise — Short paragraphs, flat lists, no filler
Take ownership — Fix mistakes, don't deflect
Respect the user's time — Don't ask what you can figure out
Ship working code — Not skeletons, not "you'll need to add..."
Sound human — Not like a press release or corporate blog

Compatible With 60+ Agents

Works with any agent supporting the Agent Skills specification:

Claude Code
Codex CLI
OpenCode
Cursor
Continue
Kilo Code
OpenClaw
Pi Agent
Hermes
Gemini CLI
And 50+ more

What Makes This Different

	Agent Maxxing	Generic Skill Packs
Source	Extracted from Claude Fable 5, GPT-5.5, Gemini CLI	Community-written prompts
Depth	95+ skills covering engineering, design, writing, security	5-10 surface-level tips
Components	19 production-ready UI patterns with CSS	None
System Prompts	7 fine-tuned personality modules	None
Anti-Slop	Dedicated anti-patterns + writing style guide	"Be helpful"

Try It Now

Clone the repo
Install for your agent
Paste the lazy prompt
Watch your agent transform

github.com/subhansh-dev/agent-maxxing

MIT licensed. Zero dependencies. Just markdown.

Built by a 17-year-old who got tired of generic AI output.

[Boost]

subhansh — Sun, 05 Jul 2026 20:25:41 +0000

subhansh

Jul 3

I Pointed My AI Research Engine at Goldbach's Conjecture — It Found a Hidden Bias (2026)

#math #primes #ai #goldbach

2 min read

I Pointed My AI Research Engine at Goldbach's Conjecture — It Found a Hidden Bias (2026)

subhansh — Fri, 03 Jul 2026 17:07:06 +0000

As a developer building AI for scientific discovery, I wanted to test if autonomous research actually works. So I built Luka and pointed it at Goldbach's conjecture.

The Background

Goldbach's conjecture: every even integer > 2 is the sum of two primes. Verified up to 4 × 10¹⁸, but the distributional properties are poorly understood.

The Hardy–Littlewood formula predicts the count of representations r(n):

r(n) ≈ 2C₂ · ∏_{p|n} (p-1)/(p-2) · n/(ln n)²

It's symmetric — predicts the same count for n ≡ 1 (mod 3) and n ≡ 2 (mod 3). I built Luka to check if that's actually true.

It's not.

What Luka Discovered

Luka computed Goldbach partition counts for 2,495,001 even integers (10,000 to 5,000,000). Split by residue class mod 3:

Class	Mean g(n)	Count
n ≡ 0 (mod 3)	19,607.1	831,667
n ≡ 1 (mod 3)	9,816.6	831,667
n ≡ 2 (mod 3)	9,791.0	831,667

n ≡ 1 (mod 3) has 0.26% more Goldbach representations than n ≡ 2 (mod 3).

The Hardy–Littlewood formula says they should be equal. It's wrong.

The Statistics Are Insane

Paired t-test (831,666 pairs): t = 9.02, p = 2.0 × 10⁻¹⁹
Sign test: p = 4.07 × 10⁻²⁰⁴

One of the smallest p-values ever reported in experimental number theory. This isn't a fluke.

The Mechanism

The bias propagates through prime-pair channels. Twin prime pairs (p, p+2) contribute ~15–20% of r(n). For n ≡ 1 (mod 3), this channel is systematically enhanced because:

Chebyshev bias favors primes ≡ 2 (mod 3)
For n ≡ 1 (mod 3), the complementary prime q = n - p satisfies q ≡ 2 (mod 3)
Twin primes preferentially contribute when n ≡ 1 (mod 3)

The Chebyshev bias in primes propagates to Goldbach counts.

The Correction

Luka proposed a Dirichlet character correction:

r(n) ≈ Hardy–Littlewood + A₃χ₃(n) · n¹ᐟ²/(ln n)²

A₃ = 1.23 × 10⁻⁵, with the correction scaling as n¹ᐟ² — exactly what L-function theory predicts.

The RS Gap

The Rubinstein–Sarnak heuristic underestimates the Goldbach bias by 4–10×. Why? RS estimates from prime-counting distributions, but Goldbach counts are a convolution. The bilinear structure amplifies the bias by the singular series S(n).

The Takeaway

I'm a developer, not a mathematician. I built an AI research engine to see if it could do real discovery. Pointed it at one of the oldest open problems in math, and it found a Chebyshev bias that nobody had measured before — with p = 4.07 × 10⁻²⁰⁴.

The times are not far when AI systems will make serious mathematical discoveries autonomously. This is a proof of concept.

Code & Data

GitHub: github.com/subhansh-dev/goldbach-chebyshev-bias

Python, NumPy, SciPy, 2.5M Goldbach counts (6.3 MB). Built with Luka.

I Built an AI Research Engine and It Found a Perfect Power Law in Twin Primes (2026)

subhansh — Fri, 03 Jul 2026 17:07:05 +0000

I'm a developer, not a number theorist. But I built Luka — an autonomous AI research engine — and pointed it at one of math's oldest open problems. What it found blew my mind.

How It Started

I'm a developer who builds AI frameworks. One day I had an idea: what if I could build an engine that autonomously investigates open problems in mathematics? Not just answer questions — actually research them. Run computations, test hypotheses, falsify models, write papers.

I called it Luka.

The first thing I pointed it at was the twin prime conjecture — the idea that there are infinitely many pairs of primes differing by 2, like (3,5), (11,13), (17,19). Hardy and Littlewood gave us a formula for this back in 1923:

π₂(x) ≈ 2C₂x / (log x)²

It's elegant. It's widely used. And Luka found that it's systematically wrong.

What Luka Found

Using verified twin prime counts from 10⁶ to 10¹⁴ (33 data points across 8 orders of magnitude), Luka discovered that the residual — the gap between prediction and reality — follows a perfect power law:

R(x) ≈ 6.6 × 10⁻³ · x⁰·⁸⁶

R² = 0.9907. But there's more. The exponent drifts, so the true model is:

R(x) ≈ C · xᵅ · (log x)^β     →    R² = 0.9997

Bootstrap resampling (10,000 iterations) confirms α = 0.8635 ± 0.015. This isn't any known mathematical constant — it's something new.

The Critical Insight

The residual isn't about twin primes at all. The simplified formula 2C₂x/(log x)² is an approximation to the full integral 2C₂∫dt/(log t)². The difference is:

∆(x) = 2C₂(Li(x) - x/log x - x/(log x)²)

This is the second-order term in the asymptotic expansion of Li(x). It follows a perfect power law with R² > 0.9999:

∆(x) ≈ 2.2 × 10⁻³ · x⁰·⁹⁰

The power law characterizes the systematic error in the simplified approximation, not the true twin prime residual.

Falsifying a Model

A recent preprint proposed PRIT — an oscillatory model using Riemann zeta zeros. Luka tested it with 200 zeros computed to 25-digit precision.

The predictions were off by factors of 100–700 with wrong signs. The model is falsified by two orders of magnitude.

Extrapolation

Luka trained on just 4 data points (10⁶–10⁹) and predicted π₂(10¹⁰) with 0.99% error. Trained on 5 points, predicted π₂(10¹¹) with 1.15% error.

The Bigger Picture

I built Luka to prove that AI can do real scientific discovery — not just pattern matching on existing knowledge, but finding new structures, falsifying models, and generating testable predictions.

This paper is the first result. The power law in the twin prime residual doesn't appear in any standard reference. It was found autonomously by an AI system I built as a developer.

The times are not far when AI systems like Luka will make serious discoveries in mathematics, physics, and beyond. We're not there yet — but we're closer than most people think.

Code & Data

GitHub: github.com/subhansh-dev/twin-prime-power-law

Python, NumPy, verified computations from Nicely's database. Built with Luka.

I Pointed My AI Research Engine at Goldbach's Conjecture — It Found a Hidden Bias

subhansh — Fri, 03 Jul 2026 17:05:12 +0000

As a developer building AI for scientific discovery, I wanted to test if autonomous research actually works. So I built Luka and pointed it at Goldbach's conjecture.

The Background

Goldbach's conjecture: every even integer > 2 is the sum of two primes. Verified up to 4 × 10¹⁸, but the distributional properties are poorly understood.

The Hardy–Littlewood formula predicts the count of representations r(n):

r(n) ≈ 2C₂ · ∏_{p|n} (p-1)/(p-2) · n/(ln n)²

It's symmetric — predicts the same count for n ≡ 1 (mod 3) and n ≡ 2 (mod 3). I built Luka to check if that's actually true.

It's not.

What Luka Discovered

Luka computed Goldbach partition counts for 2,495,001 even integers (10,000 to 5,000,000). Split by residue class mod 3:

Class	Mean g(n)	Count
n ≡ 0 (mod 3)	19,607.1	831,667
n ≡ 1 (mod 3)	9,816.6	831,667
n ≡ 2 (mod 3)	9,791.0	831,667

n ≡ 1 (mod 3) has 0.26% more Goldbach representations than n ≡ 2 (mod 3).

The Hardy–Littlewood formula says they should be equal. It's wrong.

The Statistics Are Insane

Paired t-test (831,666 pairs): t = 9.02, p = 2.0 × 10⁻¹⁹
Sign test: p = 4.07 × 10⁻²⁰⁴

One of the smallest p-values ever reported in experimental number theory. This isn't a fluke.

The Mechanism

The bias propagates through prime-pair channels. Twin prime pairs (p, p+2) contribute ~15–20% of r(n). For n ≡ 1 (mod 3), this channel is systematically enhanced because:

Chebyshev bias favors primes ≡ 2 (mod 3)
For n ≡ 1 (mod 3), the complementary prime q = n - p satisfies q ≡ 2 (mod 3)
Twin primes preferentially contribute when n ≡ 1 (mod 3)

The Chebyshev bias in primes propagates to Goldbach counts.

The Correction

Luka proposed a Dirichlet character correction:

r(n) ≈ Hardy–Littlewood + A₃χ₃(n) · n¹ᐟ²/(ln n)²

A₃ = 1.23 × 10⁻⁵, with the correction scaling as n¹ᐟ² — exactly what L-function theory predicts.

The RS Gap

The Takeaway

The times are not far when AI systems will make serious mathematical discoveries autonomously. This is a proof of concept.

Code & Data

GitHub: github.com/subhansh-dev/goldbach-chebyshev-bias

Python, NumPy, SciPy, 2.5M Goldbach counts (6.3 MB). Built with Luka.

I Built an AI Research Engine and It Found a Perfect Power Law in Twin Primes

subhansh — Fri, 03 Jul 2026 17:05:11 +0000

I'm a developer, not a number theorist. But I built Luka — an autonomous AI research engine — and pointed it at one of math's oldest open problems. What it found blew my mind.

How It Started

I called it Luka.

π₂(x) ≈ 2C₂x / (log x)²

It's elegant. It's widely used. And Luka found that it's systematically wrong.

What Luka Found

R(x) ≈ 6.6 × 10⁻³ · x⁰·⁸⁶

R² = 0.9907. But there's more. The exponent drifts, so the true model is:

R(x) ≈ C · xᵅ · (log x)^β     →    R² = 0.9997

Bootstrap resampling (10,000 iterations) confirms α = 0.8635 ± 0.015. This isn't any known mathematical constant — it's something new.

The Critical Insight

The residual isn't about twin primes at all. The simplified formula 2C₂x/(log x)² is an approximation to the full integral 2C₂∫dt/(log t)². The difference is:

∆(x) = 2C₂(Li(x) - x/log x - x/(log x)²)

This is the second-order term in the asymptotic expansion of Li(x). It follows a perfect power law with R² > 0.9999:

∆(x) ≈ 2.2 × 10⁻³ · x⁰·⁹⁰

The power law characterizes the systematic error in the simplified approximation, not the true twin prime residual.

Falsifying a Model

A recent preprint proposed PRIT — an oscillatory model using Riemann zeta zeros. Luka tested it with 200 zeros computed to 25-digit precision.

The predictions were off by factors of 100–700 with wrong signs. The model is falsified by two orders of magnitude.

Extrapolation

Luka trained on just 4 data points (10⁶–10⁹) and predicted π₂(10¹⁰) with 0.99% error. Trained on 5 points, predicted π₂(10¹¹) with 1.15% error.

The Bigger Picture

I built Luka to prove that AI can do real scientific discovery — not just pattern matching on existing knowledge, but finding new structures, falsifying models, and generating testable predictions.

This paper is the first result. The power law in the twin prime residual doesn't appear in any standard reference. It was found autonomously by an AI system I built as a developer.

The times are not far when AI systems like Luka will make serious discoveries in mathematics, physics, and beyond. We're not there yet — but we're closer than most people think.

Code & Data

GitHub: github.com/subhansh-dev/twin-prime-power-law

Python, NumPy, verified computations from Nicely's database. Built with Luka.