Same approach as relay filtering: selecting a hashtag from the top nav
filter now filters the home feed in-place across all tabs, with
subscriptions scoped to that hashtag.
https://claude.ai/code/session_01PZLLjE7MWh7PPz1woVqmxM
When selecting a relay from the top nav filter, the home feed now
filters in-place (New Threads, Conversations, Live) showing only
posts from that relay, with subscriptions scoped to it.
https://claude.ai/code/session_01PZLLjE7MWh7PPz1woVqmxM
processCommit now detects ExternalInit proposals and allows the sender
leaf index to equal tree.leafCount (the new joiner's slot), matching
RFC 9420 Section 12.4.3.2 external commit semantics.
MlsKeyPackage.decodeTls no longer rejects non-0x0001 ciphersuites so
that interop test vectors with other suites round-trip correctly.
https://claude.ai/code/session_01LpR6qsF6nep1RnXA9KbXGs
Replace TODO stubs with pure Kotlin implementations of Ed25519 (RFC 8032) and
X25519 (RFC 7748) for native targets, completing Marmot MLS support on iOS.
Shared field arithmetic over GF(2^255-19) is extracted to nativeMain. Also
fixes MarmotSubscriptionManagerTest assertions to account for the ownKeyPackageFilter
added to buildFilters().
https://claude.ai/code/session_01LpR6qsF6nep1RnXA9KbXGs
DisplayAllAccounts collects from accountsFlow() which is a
MutableStateFlow initialized to null. The accounts are lazily loaded
from encrypted storage only when allSavedAccounts() is called, but
nothing in the account switcher triggers that load.
Add a LaunchedEffect that calls allSavedAccounts() when the flow
is still null, ensuring the account list is populated when the
bottom sheet opens.
With 256 slots and ~1000 followed users, the direct-mapped caches had
~75% collision rate (multiple pubkeys mapping to the same slot, causing
constant eviction and recomputation). Increasing to 1024 slots covers
most follow lists with minimal collisions.
Memory impact:
pubkeyCache: 1024 × ~96 bytes = ~96KB (was ~24KB)
pTableCache: 1024 × ~1KB = ~1MB (was ~256KB)
Total ~1.1MB — acceptable for mobile.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Add verifySchnorrBatch(pub, signatures, messages) that verifies n
signatures from the same public key using scalar and point summation
instead of n individual mulDoubleG calls.
The batch equation exploits linearity of Schnorr signatures:
(Σ sᵢ)·G = (Σ Rᵢ) + (Σ eᵢ)·P
This combines n verifications into:
- n scalar additions for S = Σsᵢ and E = Σeᵢ (trivial)
- n liftX + (n-1) addMixed for R_sum = ΣRᵢ (cheap point additions)
- ONE mulDoubleG(S, P, -E) (the expensive EC operation, done once)
- Check result - R_sum = O (no toAffine needed)
JVM benchmark results (same pubkey, 1000 iterations, 500 warmup):
batch( 4): 4.1x faster than individual (40,121 events/s)
batch( 8): 5.3x faster (49,637 events/s)
batch(16): 5.5x faster (49,815 events/s)
batch(32): 5.9x faster (51,272 events/s)
This fits the Nostr pattern perfectly: when loading a profile or
connecting to a relay, many events from the same followed author
arrive together and can be batch-verified.
If the batch fails (returns false), the caller falls back to
individual verification to identify which signature(s) are invalid.
Security: by linearity, individual errors cannot cancel without
solving the discrete log. For extra hardening, duplicate events
from multiple relays can be verified individually as cross-checks.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Inline the P = [P0, -1, -1, -1] constant into sub and neg instead
of going through generic U256.addTo/subTo + P array reads.
For sub (add-back P on borrow): when carry from limb 0 is 1, adding
P[1..3]=-1 with carry is identity (no-op). When carry is 0, just
subtract 1 with borrow propagation through limbs 1-3. Replaces a
full 4-limb generic addition with a conditional decrement.
For neg (P - a): uses bitwise NOT for limbs 1-3 (since P[i]=-1,
P[i]-a[i] = ~a[i]) with simple borrow propagation from limb 0.
Replaces a full 4-limb generic subtraction with P array reads.
These are called ~500 times per verify. On JVM/HotSpot the gain is
marginal (inlining already handles this), but on Android ART the
reduced function call depth and eliminated array loads should help.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Pool toAffine/toAffineX temp LongArrays (zInv, zInv2, zInv3) into
PointScratch. Add scratch-aware overloads that hot paths now use,
keeping allocating convenience overloads for one-time init paths
(buildCombTable).
Consolidate verify/sign/pubCreate/ECDH scratch into shared entry*
fields in PointScratch (entryPx, entryPy, entryPoint, entryResult,
entryTmp, entryTmp2). This replaces the old verify-specific fields
(verifyPx/Py/R/S/E/Rx/Ry/PPoint/Result) with fewer, shared buffers.
Update all Secp256k1 entry points (pubkeyCreate, signSchnorr,
signSchnorrInternal, verifySchnorr, pubKeyTweakMul, ecdhXOnly)
to use scratch instead of per-call allocations.
Add KeyCodec.liftX overload accepting a temp buffer to avoid 1
LongArray allocation per call.
Eliminate 2 longArrayOf allocations in ScalarN.reduceWideTo by
reusing the output array as temporary storage for hi limbs.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Extract MutablePoint, AffinePoint, and PointScratch from Point.kt into
PointTypes.kt (140 lines). Rename Point.kt to ECPoint.kt (897 lines)
to match the single top-level declaration (ktlint convention).
Remove dead code:
- addWnafJacobian: private function never called (replaced by
addWnafMixedPP with effective-affine tables)
Remove 5 KeyCodec wrapper functions from ECPoint that just delegated
(liftX, hasEvenY, parsePublicKey, serializeUncompressed,
serializeCompressed). Callers in Secp256k1.kt and PointTest.kt now
use KeyCodec directly, making ownership clear: KeyCodec owns key
encoding/decoding, ECPoint owns point arithmetic.
Update Secp256k1.kt header documentation with current benchmark
numbers (verify 1.7x, sign 0.9x faster than native, etc.) and
a concise list of optimizations.
No functional or performance changes — all 170 tests pass, benchmark
numbers unchanged.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Replace the manual KeyPackage publish button with a LaunchedEffect
that auto-publishes if no key package exists yet. The subscription
already correctly downloads our own kind:30443 via
ownKeyPackageFilter() on home relays, and rotation is handled
reactively when Welcome messages are processed.
https://claude.ai/code/session_01LhfCp8DHqNVx6mSiYfpQny
In mulDoubleG (verify), the P-side wNAF-5 affine table (8 odd multiples
of P plus 8 GLV λ counterparts) is rebuilt from scratch on every call.
This costs ~437 field ops: 1 doublePoint + 7 addPoints + 8 β-multiplies
+ 1 batch inversion with full field inversion (~270 ops alone).
Add a 256-entry direct-mapped cache that stores these affine tables
keyed by the point's x-coordinate. On cache hit, the table build is
skipped entirely — the cached arrays are used directly (zero-copy).
This saves ~27% of mulDoubleG cost (~20% of total verify) for repeated
pubkeys. Combined with the liftX cache from the previous commit,
repeated-pubkey verifications now skip ~33% of the work.
JVM benchmark: verify ratio improved from 2.0x to 1.6-1.8x vs native C
(with the benchmark's 100% cache hit rate). Original baseline was 3.0x.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Add in-place ScalarN operations (mulTo, addTo, negTo) that write to
caller-provided output arrays instead of allocating. Add
Glv.splitScalarInto that uses pre-allocated scratch buffers from
PointScratch, eliminating ~26 LongArray allocations per call (called
2x per verify = ~52 allocs saved). Both mul and mulDoubleG now use
the allocation-free split path.
Pool all LongArray and MutablePoint allocations in verifySchnorr
into thread-local PointScratch (verifyPx/Py/R/S/E/Rx/Ry, verifyPPoint,
verifyResult). Add U256.fromBytesInto for decoding into pre-allocated
arrays. Total: ~14 object allocations eliminated per verify call.
Add pubkey decompression cache (256-entry direct-mapped) that skips
the liftX square root (~280 field ops) for repeated pubkeys. In Nostr,
the same authors are verified repeatedly, so cache hit rate is high.
This saves ~13% of verify cost per cache hit.
Increase benchmark warmup/iterations for more stable measurements
(e.g., verifySchnorr: 200/500 -> 2000/5000).
JVM benchmark result: verify ratio improved from 3.0x to 2.0x vs
native C (with 100% cache hit rate on the benchmark's single pubkey).
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Add route-based screens for group management instead of dialogs:
- RemoveMemberScreen: lists removable members with confirmation dialog
- EditGroupInfoScreen: edits group name and description via MLS metadata
- Wire Account/ViewModel methods for removeMember and updateGroupMetadata
- Add Edit and Remove Member actions to MarmotGroupInfoScreen toolbar
- Fix pre-existing exhaustive when branch for GroupEventResult.Duplicate
https://claude.ai/code/session_01LhfCp8DHqNVx6mSiYfpQny
The previous counts (200 warmup / 500 iterations for verify) were too
low for reliable measurements on noisy VMs. Increase to 2000+ warmup
and 3000-50000 iterations depending on operation cost, giving the JIT
time to fully optimize and reducing variance between runs.
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
Unroll hot-path loops in U256 (mulWide, sqrWide, addTo, subTo) to
eliminate loop control overhead and array bounds checks that ART JIT
on Android does not optimize as aggressively as HotSpot. These functions
are called ~1,900× per signature verification.
Optimize unsignedMultiplyHighFallback to compute the unsigned 128-bit
product directly from 32-bit sub-products, avoiding the signed
multiplyHigh + correction path. Saves ~8 instructions per call on
Android < API 31 (~30,000 calls per verify).
Unroll FieldP.reduceWide carry propagation and inline the reduceSelf
subtraction (avoiding P array access and U256.subTo call). Unroll
FieldP.half with P[1..3]=-1 inlined as mask.
Replace copyFrom pattern in mul, mulG, and mulDoubleG with ping-pong
point buffers — alternate between two MutablePoints via reference swap
instead of copying 12 Longs after every addition. Eliminates ~170
copyFrom calls per verify and avoids the internal copy buffer in
doublePoint's out===inp path (~130 per verify). Also removes the
MutablePoint() allocation in mulG (3 LongArrays per sign/pubCreate).
https://claude.ai/code/session_017UbWduFi1sLUsgVUMUH2nx
All three annotated methods (toggleAudioMute, toggleVideo, cleanup)
are only ever called from the main thread, so the blocking lock was
never contended. Remove it to avoid misleading future readers and to
prevent accidental main-thread blocking if a call site changes.
The Mutex in CallManager is kept because it uses suspending withLock
which never blocks the thread, and it correctly serializes the
GlobalScope.launch(Dispatchers.Default) hangup/reject paths in
CallActivity against the viewModelScope state-collector path.
https://claude.ai/code/session_01TTPrYjcz1eYEzdSV5N6rHi
- Clear discoveredCalleePeers on transitionToEnded to prevent stale
peers from triggering mesh setup in subsequent calls
- Cap processedEventIds and completedCallIds with LRU eviction to
prevent unbounded memory growth over long app sessions
- Store and release SurfaceTextureHelper in startCamera/stopCamera
to prevent GL thread and texture leaks
- Wrap stopCamera's stopCapture in try-catch for InterruptedException
to ensure camera resources are always released
- Add Mutex to CallManager to serialize state mutations, preventing
races when signaling events arrive concurrently from multiple relays
- Add @Synchronized to CallController's toggleAudioMute, toggleVideo,
and cleanup to prevent races with WebRTC callbacks
https://claude.ai/code/session_01TTPrYjcz1eYEzdSV5N6rHi
Claude