2026-08-25 05:48:20
Why episodic, semantic, procedural, and working memory should not be dumped into the same bucket.
An AI agent completes a task successfully. It finds a flight, compares the options, applies the travel policy, and books the right itinerary.
What should it remember?
Everything, of course. Embed the conversation, persist the tool calls, store the final response, the screenshots, preferences, policy documents, and all the steps taken. When a similar request comes up later, retrieve the most relevant pieces and put them back into context.
It looks like memory. In practice, it becomes a costly junk drawer.
The problem is not that it stores too few things — it treats entirely different types of information as if they did the same job. A user's seat preference is not the story of their last booking. The refund policy is not the sequence of steps to refund a flight. The agent's current scratchpad is not knowledge at all. Dump all of this into one searchable pool, and the agent gets plenty of similar-looking context but no signal for what each item means or how much to trust it.
That's not a memory system. It is storage with a search box.
Agent memory design must start with a different question: what type of memory do we want here and what decision should it inform?
"Agent memory" sounds like a single feature: add a database, connect an embedding model, done. But an effective agent needs several distinct memory functions:
While using terms from cognitive science, this analogy has many limitations I will discuss later. The practical importance is that every type of memory has different storage, retrieval, update, and expiration mechanisms. It is not just "find similar context" — the agent receives the relevant type of information with the appropriate level of authority at the right moment.
Working memory is the current workspace — the goal, plan, recent tool responses, unresolved questions. For a flight-booking agent, that might include the requested route, three fares being compared, and a verification step that is still pending. It must be small, relevant, and disposable. It is easy to confuse this memory type with the model's context window — but the context window is where information can appear, while working memory is the system deciding what deserves to occupy that space right now. Keeping every old message active does not make the agent more informed. It makes the agent easier to distract.
Episodic memory contains specific past events and the structure of those events: goal, context, action sequence, observations, and outcomes. "On July 8, the user rejected the cheapest fare because it didn't include checked bags; then the agent found itineraries with baggage included." It is a useful precedent — but just that. It does not mean that the user rejects any cheap fares just because of checked bags. It is exactly the place where a simple vector storage can cause serious problems — similarity of text can find an old experience without saying how precisely to interpret it.
Semantic memory stores facts, relationships, and preferences that can live longer than the episode that generated it — the user prefers aisle seats, flights over $800 require approval, ORD airport is O'Hare. This kind of information requires more than text. Production systems must track provenance, confidence, scope, and freshness of every piece of information stored. "User prefers aisle seats" can mean very different things if it is stated once or inferred from ten bookings — without this information, a poorly supported fact can become as authoritative as a well-supported one.
Procedural memory stores ways of acting — validated workflows, constraints, and approval thresholds. This information is not simply recalled — it actively shapes the plan. But this is also the place where luck easily becomes a policy: if every successful trajectory becomes a reusable procedure, one lucky run that skipped a required step can become a trusted workflow. The episodic memory tells us what happened in the past. Procedural memory is about the next step — and that is exactly where validation must occur.
One task can create all four kinds of memory at once: a flight-change request generates temporary state in working memory, a completed event in episodic memory, and a user preference in semantic memory ("I always prefer morning flights"), and (if this handling of a change fee proved to be consistent) a candidate update of procedural memory.
Classification is the foundation of this architecture. In practice, it means that any piece of information to be stored must have an explicit type and metadata fields before writing into the database — not inferred at the retrieval time:
{
"id": "mem_9f1a2c",
"type": "semantic",
"content": "User prefers aisle seats on flights over 3 hours.",
"scope": { "owner": "user_4821", "applies_to": "long_haul_flights" },
"provenance": {
"source": "episodic",
"derived_from": ["ep_7712", "ep_7810", "ep_8004"],
"extraction_method": "explicit_statement"
},
"confidence": 0.86,
"created_at": "2026-06-02T14:11:00Z",
"last_validated_at": "2026-07-19T09:00:00Z",
"expires_at": null,
"supersedes": null
}
It also needs a routing layer that determines where to search before determining which piece is similar — exactly what a flat vector storage omits:
def retrieve(query, task_purpose, user_id):
# 1. Determine which memory types are actually needed by this task
candidate_types = classify_purpose(task_purpose)
# e.g. "check baggage policy" -> ["semantic", "procedural"]
# "has this user done this before?" -> ["episodic"]
results = []
for mem_type in candidate_types:
hits = vector_search(
query,
filter={"type": mem_type, "scope.owner": user_id},
top_k=8
)
results.extend(hits)
# 2. Rank inside the selected subset — not inside the whole database
return rank(
results,
weights={"relevance": 0.4, "confidence": 0.3,
"recency": 0.2, "scope_match": 0.1}
)
The exact code is not the point. The design replaces "search everything, then let the model sort it out" with "classify first, search within that type, then rank." Classification happens at write time and again at retrieval time; similarity search runs only inside an already-scoped set.
This failure mode is not theoretical: it emerges every time an agent accumulates different types of memory over time, especially when the agent resumes after a break and reconstructs its state from persisted information. If all this information is in the same index, a temporary workaround created by the agent to work around a failed tool call can rank higher than the actual approved procedure for such a tool — the wording is the same, the embeddings are close, with no signal indicating which one is temporary and which one is approved. The solution is not a better vector search — it is in refusing to write the temporary solution into the same database as the official procedure.
A single searchable memory index looks very elegant: one storage and retrieval interface for everything. But the complexity does not go away; it goes into the prompt — the model needs to determine at every moment what the retrieved piece is: a fact, an anecdote, an instruction, an obsolete preference, or simply a guess. Similarity is useful, but it does not guarantee the authority, scope, and purpose of retrieved information on its own.
It is not necessary to use four different database products — a separation can be logical, not physical. What is important is the contract:
Let's be honest: the human-like memory is good up to a certain point. Human memory consolidates during sleep, decays gradually, and rebuilds on recall imperfectly. Agent memory does none of that by default: every write is intentional, every read is a query, and nothing expires unless the architecture says it should.
This is the advantage: the memory system of the agent can enforce discipline a human brain never had — every fact has provenance, every procedure has an audit trail, there is a hard expiration date instead of natural forgetting. The taxonomy is useful. The anthropomorphism beyond it mostly gets in the way.
A vector database finds similar episodes. A graph represents entities and relationships in semantic memory. A relational database stores structured procedural versions with audit history. Most serious systems use different techniques — but none of them decides, on its own, whether the specific event is a precedent, a fact, a procedure, or noise. This is a responsibility of the layer on top of the database: classification, write rules, search routing, validation, promotion, forgetting.
The agent doesn't need one big memory storage. It needs the right memory to appear at the right moment, with the right level of authority — and when something goes wrong, it needs to determine what part of the information led to that failure: experience, fact, procedure, or the current state of the agent. This is the difference between just storing the past and learning from it.
But storing the right thing in the right bucket only solves half of the problem: the harder question remains: which memories deserve to become knowledge? A correct conclusion can come from a clean trajectory, a corrected mistake, or just a lucky guess — and if the system cannot distinguish these situations, even a well-structured memory architecture preserves the wrong lessons. Memory needs evaluation before it can become knowledge. That is the next piece.
2026-08-25 05:16:55
A worker blocks on a channel nobody reads, the handler that spawned it returns, and your process now carries a goroutine that will live until the next deploy. Nothing in your logs mentions it. Do that once per request, and you have a memory graph that climbs for days before anyone opens an issue.
Go 1.27, released on August 19, shipped a runtime-level answer: a new pprof profile called goroutineleak that asks the garbage collector to prove which goroutines can never run again. It existed behind GOEXPERIMENT=goroutineleakprofile in Go 1.26; in 1.27 the experiment flag is gone, and the profile is available by default, with an HTTP endpoint at /debug/pprof/goroutineleak. The runtime does the detection work each time you collect the profile, never on a background timer
Runtime leak detection with no third-party library and no test harness is a bold claim. I also have a personal stake: a few months ago, I built my own live goroutine leak detector because the stable runtime tooling didn’t offer this (the profile existed then, but only if you built with the Go 1.26 experiment flag). So, I wrote ten small programs, each planting one goroutine leak I have met in real codebases, and asked the new profile to find them.
It found eight of them, and the two it missed taught me more about the design than the eight it caught.
All ten programs live in github.com/rezmoss/go127-goroutine-leaks, one folder per leak, runnable with go run. Every output in this article comes from those exact files running under go1.27.0 darwin/arm64 on an M-series MacBook
The design comes from Vlad Saioc’s work at Uber (proposal #74609), where the technique flagged a few hundred syntactically distinct leaks across 3,111 test suites, plus three more in a production service.
The idea reuses machinery the runtime already has. When you collect the profile, the runtime triggers a dedicated garbage collection cycle. Marking starts from the runnable goroutines only. Then it looks for goroutines blocked on a concurrency primitive that marking has reached: a channel, a sync.Mutex, a sync.WaitGroup, a sync.Cond. Those goroutines could still wake, so they become roots too and marking resumes from them. That repeats until nothing new can wake.
Whatever is still unmarked when marking stops is out of reach of anything that could ever run, so nothing is left to perform the operation it waits for: the receive that would complete a send, the send or close that would wake a receiver, the Done that would finish a WaitGroup, the Signal that would wake a Cond, the Unlock that would release a mutex. Those goroutines are provably stuck, and the profile reports each one with a (leaked) marker in its state:
goroutine 19 [chan send (leaked)]:
Reachability analysis buys the detector something most leak-hunting tools lack: zero false positives, since a goroutine it reports can never make progress again. That same proof standard costs it two of my ten leaks.
Collecting the profile takes one line:
// full dump with (leaked) markers, human readable
pprof.Lookup("goroutineleak").WriteTo(os.Stdout, 2)
debug=1 prints only the leaked goroutines, aggregated by stack with counts. debug=2 prints every goroutine, marking the leaked ones. Without a debug parameter, you get the binary protobuf format for go tool pprof. In a service, you skip the code and hit the endpoint that net/http/pprof now registers for free.
Each example is an independently runnable main package. The pattern is the same in all of them: plant the leak, give the goroutine time to start and settle into its blocking or polling state, dump the profile, count (leaked) markers.
I see this one in code review more than any other. A worker sends its result on an unbuffered channel, the caller gives up after a timeout, and the send blocks forever:
func fetchQuote() string {
ch := make(chan string) // unbuffered
go func() {
time.Sleep(50 * time.Millisecond) // slow backend call
ch <- "the market is up" // BUG: nobody will ever receive this
}()
select {
case q := <-ch:
return q
case <-time.After(10 * time.Millisecond):
return "no quote available" // caller walks away
}
}
Once fetchQuote returns, the blocked sender is the only thing left holding ch. No goroutine that can still run, or that anything running could wake, has a way to reach it. The runtime agrees:
goroutine 19 [chan send (leaked)]:
main.fetchQuote.func1()
01-forgotten-sender/main.go:23
Caught. The fix is a buffered channel (make(chan string, 1)) so the send completes, and the goroutine exits, or a context.Context the worker respects.
Leak #2 mirrors the first. A consumer ranges over a channel, the producer finishes, and returns, and nobody calls close:
func processBatch(items []string) {
events := make(chan string)
go func() {
for e := range events { // loop never ends: channel never closed
fmt.Println("processed:", e)
}
}()
for _, it := range items {
events <- it
}
// BUG: missing close(events)
}
goroutine 19 [chan receive (leaked)]:
main.processBatch.func1()
Caught. One defer close(events) in the producer ends the range loop and the goroutine with it.
A struct with a channel field nobody initialized. Receiving from a nil channel blocks forever by language definition:
type worker struct {
done chan struct{} // zero value is nil
}
func startWorker() {
w := &worker{} // BUG: forgot make(chan struct{})
go func() {
<-w.done // never proceeds
fmt.Println("worker shut down cleanly")
}()
}
I expected this one to be an edge case since there is no channel object for the GC to trace. The runtime handles it with a dedicated state:
goroutine 19 [chan receive (nil chan) (leaked)]:
Caught. The runtime knows a nil-channel operation can never complete, so it never reaches for the reachability analysis.
Two goroutines, each waiting to receive from the other before sending. Go’s classic deadlock detector fires when the whole process is blocked, so in a real service, this pair sits there unnoticed:
func startPair() {
aToB := make(chan int)
bToA := make(chan int)
go func() {
v := <-bToA // waits for B, but B is waiting for us
aToB <- v + 1
}()
go func() {
v := <-aToB
bToA <- v + 1
}()
}
goroutine 19 [chan receive (leaked)]:
goroutine 20 [chan receive (leaked)]:
Caught both goroutines. The two channels form a cycle reachable only from the two blocked goroutines, and the reachability analysis walks straight through it.
This case impressed me most: partial deadlocks are invisible to the runtime’s all-goroutines-blocked check, and finding them meant manual goroutine-dump analysis or a test-time leak checker.
An error path returns before wg.Done():
func runJobs(jobs []string) {
var wg sync.WaitGroup
for _, job := range jobs {
wg.Add(1)
go func() {
if job == "corrupt" {
return // BUG: skips wg.Done()
}
defer wg.Done()
fmt.Println("finished:", job)
}()
}
go func() {
wg.Wait() // counter stuck at 1
fmt.Println("all jobs complete")
}()
}
goroutine 22 [sync.WaitGroup.Wait (leaked)]:
Caught. Put defer wg.Done() on the first line of the worker, before any early return can bypass it. Or on Go 1.25+, use wg.Go(func() { ... }) and let the standard library pair Add with Done for you.
An error path returns while holding a lock, no defer in sight. Every later goroutine that wants the lock joins a queue that never moves:
c.mu.Lock()
if err := loadFromDB(c); err != nil {
fmt.Println("refresh failed:", err)
// BUG: returns while still holding c.mu
} else {
c.mu.Unlock()
}
go func() {
c.mu.Lock() // blocks forever
defer c.mu.Unlock()
fmt.Println("cache entries:", len(c.data))
}()
goroutine 19 [sync.Mutex.Lock (leaked)]:
Caught. defer mu.Unlock() on the line after Lock() remains the idiom that best survives refactoring.
A consumer waits on a sync.Cond for work. The producer code that used to call Signal was refactored away:
ready := sync.NewCond(&mu)
go func() {
mu.Lock()
for len(queue) == 0 {
ready.Wait() // nobody will ever Signal
}
// ...
}()
goroutine 19 [sync.Cond.Wait (leaked)]:
Caught. The release notes list sync.Cond as a supported primitive and the experiment confirms it.
Adapted from the “stopping short” problem in the Go blog’s pipelines article. A generator streams values; the consumer takes three and breaks without cancelling anything:
func generate() <-chan int {
ch := make(chan int)
go func() {
for i := 1; ; i++ {
ch <- i * i // BUG: no context, no done channel
}
}()
return ch
}
squares := generate()
for v := range squares {
out = append(out, v)
if len(out) == 3 {
break // generator is now stuck mid-send
}
}
goroutine 19 [chan send (leaked)]:
main.generate.func1()
Caught. A pipeline stage needs a way to hear that its consumer left; put a context.Context case in the select around the send.
A cleanup goroutine waits for a shutdown signal on a package-level channel, and no code path ever closes it:
var shutdown = make(chan struct{}) // package-level
func startCleanupWorker() {
go func() {
<-shutdown // no code ever closes this
fmt.Println("running cleanup before exit")
}()
}
The goroutine shows up in the dump, blocked on chan receive, but without the marker:
goroutine 19 [chan receive]:
main.startCleanupWorker.func1()
---- runtime reported 0 leaked goroutine(s) ----
Missed. And the runtime is right to miss it by its own rules. shutdown is a global, so it stays reachable forever, and the detector cannot prove that no future code will close it. I know nothing ever will because I wrote the program. The GC only knows what the object graph says. The release notes name this exact case as a limitation: leaks blocked on primitives reachable through globals may go unreported.
Signal channels stored in package-level variables sit outside the detector’s vision, and the release notes name a second blind spot in the same sentence: primitives still held by local variables of runnable goroutines. Passing shutdown channels and contexts as arguments helps, but only once nothing global and no still-runnable goroutine retains a reference to the primitive.
A goroutine polls a flag in a sleep loop, and the code that was supposed to set the flag is gone:
var stop atomic.Bool // nothing ever calls stop.Store(true)
go func() {
for !stop.Load() {
time.Sleep(10 * time.Millisecond)
}
}()
goroutine 5 [sleep]:
---- runtime reported 0 leaked goroutine(s) ----
Missed, and this one is definitional. The detector looks for goroutines blocked on concurrency primitives. A sleeping poller is never blocked in that sense; every 10ms it wakes, checks, and sleeps again. From the scheduler’s point of view, it is a healthy, hardworking goroutine. It will also still be checking that flag when your process gets its SIGTERM three weeks from now.
Polling loops, forgotten time.Ticker consumers and tight for {} spins all live in this category. The plain goroutine profile still shows them, and grouping that dump by stack over time is what goroscope does, so I get to keep my tool for the leaks the runtime refuses to name.
|
# |
Leak |
Verdict |
|---|---|---|
|
1 |
Forgotten sender after timeout |
Caught |
|
2 |
Receiver on a never-closed channel |
Caught |
|
3 |
Receive on a nil channel |
Caught |
|
4 |
Two-goroutine deadlock cycle |
Caught (both) |
|
5 |
WaitGroup with a missing Done |
Caught |
|
6 |
Mutex held by a dead code path |
Caught |
|
7 |
Cond. Wait with no signaler |
Caught |
|
8 |
Pipeline stage without cancellation |
Caught |
|
9 |
Wait on a global channel |
Missed |
|
10 |
Sleep-loop poller |
Missed |
Eight out of ten, nine leaked goroutines reported across the ten programs, zero false positives. Both misses are documented behavior rather than bugs, and both follow from the same design choice: the detector only reports goroutines it can prove are stuck.
Import net/http/pprof as usual, and the endpoint appears alongside the profiles you already know. My test service leaks one worker per timed-out request:
func lookupPrice(w http.ResponseWriter, r *http.Request) {
result := make(chan string)
go func() {
time.Sleep(2 * time.Second) // slow upstream
result <- "42.00" // BUG: handler is long gone
}()
select {
case price := <-result:
fmt.Fprintln(w, "price:", price)
case <-time.After(100 * time.Millisecond):
http.Error(w, "upstream timeout", http.StatusGatewayTimeout)
}
}
Three curls and one profile later:
$ curl "localhost:8080/debug/pprof/goroutineleak?debug=1"
goroutineleak profile: total 3
3 @ 0x... 0x... 0x... 0x... 0x...
# main.lookupPrice.func1+0x3b server/main.go:38
Three requests, three leaks, one aggregated stack pointing at the exact line. go tool pprof -http=:0 http://host/debug/pprof/goroutineleak works too and opens the web UI, flame graph included.
One caution before you copy the demo: it binds to 127.0.0.1 on purpose, and your service should be as careful. Keep /debug/pprof/ off the public mux, behind an internal listener or auth, since profiles expose stack detail and this one triggers a GC cycle on request.
One operational detail bit me during testing. My first profile came back with total 0 because the abandoned workers were still inside their two-second time.Sleep. A goroutine only qualifies once it blocks on the primitive, so you see a leak from a slow upstream call only after that call finishes. Long-running processes make settled leaks likely, but a profile is still a point-in-time view: workers created moments ago may not have blocked yet, so if you script a check, give the leaks time to settle.
Collection cost is a dedicated GC cycle per profile, so treat it like a heap profile: collect on demand first, measure what the extra GC cycle costs on your heap before putting it on a timer, and never call it in a hot loop…
Keep two habits from the misses. Avoid parking cancellation primitives in globals or in goroutines that stay runnable forever; passing ownership through arguments helps the reachability analysis once those other references go away. And keep an eye on total goroutine count, because pollers and tickers age outside the detector’s definition of stuck.
Go has had goroutine leaks since day one, and from 1.27 on the runtime will name the ones it can prove are stuck!
Happy coding!
2026-08-25 05:01:56
The cryptocurrency market operates 24 hours a day, seven days a week, creating both opportunities and challenges for traders.
Unlike traditional financial markets, crypto markets never close. Monitoring price movements, analyzing market data, and executing strategies manually can be extremely time-consuming.
This has accelerated the development of AI crypto trading bots and
Modern AI trading platforms are no longer limited to simple rule-based automation. Advanced solutions now integrate:
In 2026, AI-powered trading technology is becoming an important part of the evolution of digital asset markets.
Among emerging AI trading platforms, MillionPool stands out as a leading AI quantitative trading platform, offering users access to structured AI-powered Quant Pool strategies and automated trading solutions.
An AI crypto trading bot is an automated software system that uses artificial intelligence and algorithmic strategies to analyze cryptocurrency markets and execute trading decisions.
Traditional trading bots usually operate based on predefined rules, such as:
AI-powered trading systems introduce more advanced capabilities, including:
Instead of relying completely on manual decisions, AI trading platforms use technology to improve efficiency and automation.
However, AI trading tools should be viewed as technology solutions rather than guaranteed profit systems. Market conditions, volatility, and strategy design continue to influence trading outcomes.
A modern AI crypto trading platform generally includes four key components.
AI systems collect and analyze large volumes of market information, including:
This allows algorithms to identify patterns that may be difficult to process manually.
Quantitative trading uses mathematical models and statistical methods to evaluate market conditions.
AI quantitative models may analyze:
This creates a systematic approach to strategy development.
Once a strategy identifies specific market conditions, automated systems can execute predefined actions.
Automation helps reduce:
Advanced AI trading systems continuously evaluate strategy performance.
Monitoring may include:
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Cryptohopper is an automated crypto trading platform that provides customizable trading tools.
Key features include:
It is suitable for traders who prefer building and adjusting their own strategies.
3Commas provides automated trading tools designed to help users manage crypto trading strategies across multiple exchanges.
Features include:
Pionex integrates automated trading bots directly into its platform.
Popular features include:
Coinrule allows users to create automated trading strategies without writing code.
Features include:
|
Platform |
Best For |
AI Capability |
Automation Level |
|---|---|---|---|
|
MillionPool |
⭐⭐⭐⭐⭐ |
⭐⭐⭐⭐⭐ |
|
|
Cryptohopper |
Strategy Customization |
⭐⭐⭐⭐ |
⭐⭐⭐⭐ |
|
3Commas |
Trading Management |
⭐⭐⭐⭐ |
⭐⭐⭐⭐ |
|
Pionex |
Beginners |
⭐⭐⭐ |
⭐⭐⭐⭐ |
|
Coinrule |
No-Code Automation |
⭐⭐⭐ |
⭐⭐⭐⭐ |
Selecting the right AI trading platform requires evaluating several factors.
A professional AI trading platform should provide:
Users should evaluate:
Important factors include:
A reliable AI trading platform should focus on technology development and realistic expectations rather than unrealistic promises.
AI crypto trading bots are powerful technology tools, but they are not guaranteed profit systems.
Their performance depends on:
The main advantages of AI trading systems are:
Users should always consider market risks before using any automated trading technology.
Artificial intelligence is becoming an important technology in financial markets.
Future AI trading platforms may continue developing through:
The combination of AI and quantitative finance is expected to create more accessible and sophisticated trading technologies.
An AI crypto trading bot is an automated system that uses artificial intelligence, quantitative models, and algorithmic strategies to analyze cryptocurrency markets and execute trading decisions.
Unlike traditional bots based on fixed rules, AI trading systems can process large amounts of market data, identify patterns, and optimize strategy execution through advanced analytics.
AI quantitative trading uses mathematical models, statistical analysis, and artificial intelligence algorithms to evaluate market conditions.
Traditional crypto trading often relies on manual research and human decisions, while AI quantitative trading focuses on:
AI does not eliminate market risks but provides advanced tools for improving trading efficiency.
When choosing an AI crypto trading platform, users should evaluate:
A professional platform should provide reliable technology, clear information, and realistic expectations.
The rise of artificial intelligence is transforming how people interact with cryptocurrency markets.
Among emerging AI trading platforms, MillionPool provides a modern approach by combining AI technology, quantitative trading models, and automated strategy execution.
For users interested in exploring AI-powered crypto trading solutions, MillionPool represents one of the leading platforms to consider in 2026.
As AI technology continues to evolve, automated quantitative trading will likely become an increasingly important part of the future financial ecosystem.
Disclaimer:
AI trading technology involves market risks. This article is for educational and informational purposes only and does not constitute financial advice.
This story was published as a press release by Btcwire under HackerNoon’s Business Blogging
2026-08-25 04:46:17
Grand Cayman, Cayman Islands, August 24th, 2026/Chainwire/--The
CEO of the parent company, Holonym Foundation, Dr. Shady El Damaty said, “This is the first chain-agnostic and decentralized wallet protocol that does not lock you into an enterprise landlord to rent your users’ keys from. We support EVM, Sui, and Solana chains today, but with Ika we can support every single chain, allowing app developers to quietly push complexity to the back-end and focus on user experience.”
The new human.tech Wallet Protocol expansion marks a major milestone in commercial applied cryptography. It is the first commercial application of 2PC-MPC threshold signing between two parties, where one is a standard enclave, and the other (Ika) is a decentralized multi-party computation network that can scale to 100+ nodes. The move challenges successful incumbents such as Privy and Fireblocks by offering greater assurances against lock-in to rent-seeking providers and service downtime.
“Our wallet protocol is incredibly well suited for agentic payments and finance. We designed the system with rigorous automation safeguards to meet government and enterprise standards,” explained Nanak Nihal Khalsa, CTO and architect of the system.
The 
Incumbent providers such as Privy or Fireblocks charge hefty fees for monthly active users or transaction volume, stifling margins and product growth for developers. These service providers now need to make a strong case why developers should not switch to a cost-optimized service without lock-in requirements and greater composability. The standard human.tech Wallet Protocol is currently free to use at any volume with ready-to-use migration tools from existing service providers.
For DeFi, consumer, RWA, stablecoin, prediction market, trading, and social app builders, the
"The hard part of agentic payments isn't speed, it's authority," said Omer Sadika, Co-Founder of Ika. "Software acting on someone's behalf needs bounded permission, not an omnipotent private key. Squid Mode gives human.tech's developers exactly that: policy enforced at the moment of signing by a decentralized network, on every chain their users touch, with no operator on either side able to act alone."
The human.tech Wallet Protocol “Squid Mode” is live today. The WaaP CLI and SDK are on npm:
About the human.tech Wallet Protocol
human.tech is the identity and control layer between people, institutions, and AI. It proves someone is a real, unique person without exposing anything else about them, and it bounds what software may do on their behalf. More than 51 million credentials have been issued to 2.1 million accounts, and the stack has protected over $512 million in value against bot and duplicate-account attacks. human.tech is built by Holonym Foundation, a Delaware public benefit company founded in 2022.
Ika is the network behind Bridgeless Capital Markets. Powering dWallets, Ika lets assets from any network be held, traded, and put to work on any other - no bridges, no wrapped tokens. By turning wallet control and signing authority into decentralized, programmable infrastructure, Ika gives developers a new primitive for building the next generation of trading, custody, treasury, payments, and multi-chain financial applications. Learn more at
human.tech medid
This story was published as a press release by Chainwire under HackerNoon’s Business Blogging
2026-08-25 04:36:49
Seoul, South Korea, August 24th, 2026/Chainwire/--
The preview runs on StreamChain, an EVM-compatible mainnet founded by GW Lee that FlyEdge says was built specifically for aviation loyalty. The company said the network’s aviation-focused architecture and ready-made interfaces were key reasons for choosing it over a general-purpose blockchain.
At the center of the preview is airline control. Through a token factory, airlines can issue an initial supply, mint additional tokens, burn tokens, and transfer them. Transactions are verified through on-chain receipts and finality.
That control addresses one of the biggest concerns surrounding tokenized airline loyalty.
Airlines have traditionally been cautious about making loyalty currencies more liquid because greater circulation could affect breakage—miles that are issued but never redeemed, which can represent an important part of loyalty-program economics. Earlier experiments, including Air France-KLM’s “Miles on Blockchain” proof of concept in 2017–2018, demonstrated interest in blockchain while also highlighting the business challenges surrounding airline loyalty.
FlyEdge’s model is designed to keep those decisions with the airline. Airlines retain control over official value, permitted conversions, and the services for which tokens can be used, while FlyEdge provides a shared infrastructure layer for authentication, ledger functions and settlement.
The company is also attempting to remove the technical friction normally associated with blockchain. Traveler accounts are non-custodial smart accounts protected by passkeys, while network fees are sponsored. Users therefore do not need to manage seed phrases, gas tokens or traditional crypto wallets.
Released in 12 languages on 20 August 2026, the preview is built around four traveler-facing experiences: PASSPORT, a non-custodial wallet and access pass; SHOW, featuring aviation-focused live streams and sponsorship; LAST CALL, centered on imminent seat experiences; and LOUNGE, where users can convert permitted airline tokens and request airline services.
FlyEdge says it has verified 100 pilot assets and activity across all four experiences on-chain, with the results cross-checked through a public blockchain explorer.
The company stresses that the preview does not represent existing commercial deployment by external airlines. It is an invitation for airlines, travelers and media organizations to test and verify the system themselves.
FlyEdge also does not claim that tokenization eliminates airline liabilities. Accounting treatment, it says, remains a decision for each airline in consultation with its auditors and regulators.
“Airline loyalty doesn’t need another general-purpose chain. It needs infrastructure that understands how airlines actually operate,” said John Kim, founder and chief technology officer of FlyEdge. “Rather than asking airlines and the media to take our word for it, we’re asking them to verify the entire cycle themselves.”
FlyEdge’s broader ambition is to build an aviation settlement layer, rather than simply create another mileage token. Its vision is to make airline loyalty value an asset that airlines and travelers can move, use and audit while airlines retain control over their own loyalty economics.
The company’s tagline captures the idea: “Unlocking the True Value of the Skies.”
FlyEdge is an aviation-focused B2B infrastructure platform built on StreamChain, designed to unlock the true value of tokenized airline loyalty. Founded by John Kim, who also serves as the company’s chief technology officer, FlyEdge bridges enterprise-grade controls for airlines with frictionless, passkey-secured non-custodial accounts for travelers, providing a scalable settlement layer for the aviation industry. To explore the public preview and learn more, visit
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GW Lee
This story was published as a press release by Chainwire under HackerNoon’s Business Blogging
2026-08-25 04:00:03
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Again, Take These Lessons With You](https://hackernoon.com/when-we-can-pitch-startups-at-events-again-take-these-lessons-with-you-in2jc3yio)
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