Soreno & Consensus
Consensus
Hey Soreno, I’ve been mulling over the idea of building a little tool that helps teams find consensus more smoothly—something that nudges conversations so everyone feels heard while still pushing for concrete decisions. What’s your take on using an algorithm to balance the flow of ideas?
Soreno
That sounds like a solid problem to tackle. If you treat each input as a weighted contribution, you could run a small clustering algorithm to surface the main themes before letting people vote on them. A simple TF‑IDF on the discussion plus a quick k‑means on the vectors could give you a shortlist of “hot” topics. Then, add a rule that limits how often the same voice can dominate a turn—some kind of token bucket per user. That way the algorithm nudges people toward the next item, keeps the flow moving, and makes sure nobody gets stuck on a single idea for too long. It’s a mix of NLP to capture content and a tiny state machine to keep the cadence. You’d just need to tweak the thresholds until the rhythm feels natural for the team. Happy to help you sketch out the details.
Consensus
That’s a clever mix. I’d start by pulling a small sample of chats to test the TF‑IDF+K‑means pipeline and see what the “hot” topics actually look like. For the token‑bucket, decide on a max turn count—maybe two consecutive turns per user before the system nudges someone else. Also think about a grace period; if a topic needs deeper digging, it shouldn’t be cut off too early. We could prototype a tiny widget that pops up after each turn, showing the current score and suggesting the next item. Once the flow feels natural, we tweak the thresholds. Want me to draft the skeleton code for the clustering part?
Soreno
Sounds solid—go ahead and sketch the clustering skeleton. I’ll keep an eye on the token bucket logic and tweak the grace period once you see how it behaves in a real chat. Keep it lean, and we’ll iterate from there.
Consensus
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
def cluster_messages(messages, n_clusters=5, max_iter=100):
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(messages)
km = KMeans(n_clusters=n_clusters, max_iter=max_iter, random_state=42)
km.fit(X)
clusters = {}
for idx, label in enumerate(km.labels_):
clusters.setdefault(label, []).append(messages[idx])
return clusters
Soreno
Nice start—just a couple of tweaks. First, drop the `max_iter` default; 100 is fine, but if you want tighter convergence, set `n_init=10` to get a stable centroid set. Also, add a small `min_df=2` to the vectorizer so single‑shot words don’t skew the cluster. Finally, return not only the clusters but also the centroid terms so you can see the “hot” topics directly. Here’s a quick patch:
```python
def cluster_messages(messages, n_clusters=5, random_state=42):
vectorizer = TfidfVectorizer(stop_words='english', min_df=2)
X = vectorizer.fit_transform(messages)
km = KMeans(n_clusters=n_clusters, n_init=10, random_state=random_state)
km.fit(X)
# get top terms per cluster
terms = vectorizer.get_feature_names_out()
cluster_terms = {i: [terms[ind] for ind in km.cluster_centers_.argsort()[-5:][::-1]]
for i in range(n_clusters)}
clusters = {i: [] for i in range(n_clusters)}
for idx, label in enumerate(km.labels_):
clusters[label].append(messages[idx])
return clusters, cluster_terms
```
That way you can log the top five words per cluster and confirm you’re surfacing the right themes. Let me know how it plays out.
Consensus
Looks good—just a tiny tweak to keep the imports clean. I’ll drop the `random_state` from the vectorizer since it doesn’t do anything there, and add a small sanity check so if `n_clusters` is larger than the number of unique messages it just returns what it can. Also maybe expose the `min_df` as a parameter so you can dial it down if the chat is really sparse. Other than that, that snippet should give us the cluster lists and the top terms right out of the gate. Let’s run it on a recent thread and see what the “hot” topics look like.